WO2022021224A1 - Sample classification method and apparatus, and storage medium - Google Patents

Abstract

A sample classification method (100) and apparatus (500), and a storage medium. The sample classification method (100) comprises: acquiring a sample image (S1) of a sample to be classified, and displaying the sample image (S1) on a first display interface (S110); receiving a classification instruction for classifying said sample corresponding to the sample image (S1)(S120); on the basis of the classification instruction, displaying alternative classification results of said sample in the peripheral area of the sample image (S1), such that a user selects a final classification result of said sample (S130); and receiving a selection instruction of the user for an alternative classification result, and on the basis of the selection instruction, acquiring the final classification result of said sample (S140). The alternative classification results of said sample are displayed in the peripheral area of the sample image (S1) of said sample, such that the user can quickly select the final classification result of said sample from the alternative classification results, thereby improving the efficiency of sample classification; moreover, the user operation is simple, thereby reducing the possibility of a classification error.

Description

Sample classification method, device and storage medium

manual

technical field

The present application relates to the technical field of sample classification of chip readers, and more particularly to a sample classification method, device and storage medium.

Background technique

The current blood cell classification method usually scans, divides, and generates blood cell graphics automatically for the patient's blood samples prepared as blood smears by a chip reader, and gives the original classification results. Graphical reclassification.

At present, when reclassifying blood cells, users mainly do it in the following ways: 1) Drag the cell image to the corresponding parameter row in the parameter table; 2) Drag the cell image to the image area of the target classification; 3) Right-click the cell image to pop up Drop-down box, select the target category in the drop-down box. The above-mentioned first two methods drag a long distance, which is inconvenient to operate, and the first method has many rows of parameters in the table, which is easy to be dragged into the wrong classification; the above-mentioned third method displays all the classification items in the drop-down box, which cannot be quickly Finding and selecting target types is inefficient and prone to misclassification.

SUMMARY OF THE INVENTION

The present application has been made to solve at least one of the above-mentioned problems. According to an aspect of the present application, a sample classification method is provided, the method includes: acquiring a sample image of a sample to be classified, displaying the sample image on a first display interface; receiving a sample image corresponding to the sample image to be classified A classification instruction for classifying samples; based on the classification instruction, the candidate classification results of the samples to be classified are displayed in the surrounding area of the sample image, so that the user can select the final classification results of the samples to be classified; The selection instruction of the candidate classification result is obtained, and the final classification result of the sample to be classified is obtained based on the selection instruction.

According to another aspect of the present application, a sample classification apparatus is provided, the apparatus includes a memory, a processor and a display, wherein the memory is used for storing a program and a sample image of a sample to be classified, the display is used for display under the control of the processor, the processor is configured to run a program in the memory to perform the following steps: acquiring a sample image of the sample to be classified, and displaying the sample on the first display interface by the display image; receive a classification instruction for classifying the to-be-classified sample corresponding to the sample image; based on the classification instruction, control to display the alternative classification result of the to-be-classified sample in the surrounding area of the sample image for the user Selecting the final classification result of the sample to be classified; receiving a selection instruction of the candidate classification result from the user, and obtaining the final classification result of the sample to be classified based on the selection instruction.

According to another aspect of the present application, a storage medium is provided, and a computer program is stored on the storage medium, and the computer program executes the above-mentioned sample classification method when running.

The sample classification method, device, and storage medium according to the embodiments of the present application display the candidate classification results of the samples to be classified in the surrounding area of the sample image of the samples to be classified, so that the user can quickly select the candidate classification results of the samples to be classified from the alternative classification results. The final classification result improves the efficiency of sample classification, and the user operation is simple, reducing the possibility of classification errors.

Description of drawings

FIG. 1 shows a schematic flowchart of a sample classification method according to an embodiment of the present application.

FIG. 2 shows an example of displaying alternative classification results for samples to be classified by the sample classification method according to an embodiment of the present application.

3A and 3B illustrate another example in which the sample classification method according to the embodiment of the present application displays alternative classification results for the samples to be classified.

4A and 4B illustrate yet another example of displaying alternative classification results for samples to be classified by the sample classification method according to an embodiment of the present application.

FIG. 5 shows a schematic block diagram of a sample classification apparatus according to an embodiment of the present application.

FIG. 6 shows an application example of the sample classification method according to an embodiment of the present application—a schematic structural diagram of a slicer.

detailed description

In order to make the objectives, technical solutions and advantages of the present application more apparent, the exemplary embodiments according to the present application will be described in detail below with reference to the accompanying drawings. Obviously, the described embodiments are only a part of the embodiments of the present application, rather than all the embodiments of the present application, and it should be understood that the present application is not limited by the example embodiments described herein. Based on the embodiments of the present application described in the present application, all other embodiments obtained by those skilled in the art without creative efforts should fall within the protection scope of the present application.

In the following description, numerous specific details are set forth in order to provide a more thorough understanding of the present application. It will be apparent, however, to one skilled in the art that the present application may be practiced without one or more of these details. In other instances, some technical features known in the art have not been described in order to avoid confusion with the present application.

It should be understood that the application may be embodied in different forms and should not be construed as limited to the embodiments set forth herein. Rather, these embodiments are provided so that this disclosure will be thorough and complete, and will fully convey the scope of this application to those skilled in the art.

The terminology used herein is for the purpose of describing particular embodiments only and is not intended to be limiting of the application. As used herein, the singular forms "a," "an," and "the/the" are intended to include the plural forms as well, unless the context clearly dictates otherwise. It should also be understood that the terms "compose" and/or "include", when used in this specification, identify the presence of stated features, integers, steps, operations, elements and/or components, but do not exclude one or more other The presence or addition of features, integers, steps, operations, elements, parts and/or groups. As used herein, the term "and/or" includes any and all combinations of the associated listed items.

For a thorough understanding of the present application, detailed steps and detailed structures will be presented in the following description, so as to explain the technical solutions proposed by the present application. The preferred embodiments of the present application are described in detail below, however, the present application may have other embodiments in addition to these detailed descriptions.

First, a sample classification method according to an embodiment of the present application will be described with reference to FIG. 1 . FIG. 1 shows a schematic flowchart of a sample classification method 100 according to an embodiment of the present application. As shown in FIG. 1, the sample classification method 100 includes the following steps:

In step S110, a sample image of the sample to be classified is acquired, and the sample image is displayed on the first display interface.

In the embodiment of the present application, the sample image of the sample to be classified is displayed on the first display interface. Here, the term "first display interface" is so named to distinguish it from the "second display interface" that will appear later for displaying alternative classification results of samples to be classified, and has no other limiting meaning. In the embodiment of the present application, the first display interface and the second display interface may be different display interfaces, or may be different states of the same display interface.

In step S120, a classification instruction for classifying the to-be-classified sample corresponding to the sample image is received.

In this embodiment of the present application, a classification instruction for classifying samples to be classified and inputted by a user through an input device (such as a touch screen of a display interface, a mouse and a keyboard, etc.) can be received, such as dragging an image of the sample to be classified, performing a dragging instruction, long pressing commands, click commands, etc. The dragging instruction may include: dragging the sample image of the sample to be classified by a preset distance, and the preset distance may be a small value, so as to avoid the inconvenience and error-prone problems caused by long-distance dragging. Based on the classification instruction, alternative classification results of the samples to be classified can be obtained and displayed, as will be described in the following steps.

In step S130, the candidate classification result of the sample to be classified is displayed in the peripheral area of the sample image based on the classification instruction, so that the user can select the final classification result of the sample to be classified.

In the embodiment of the present application, based on the classification instruction received in step S120, an alternative classification result of the sample to be classified may be obtained first. In one example, the candidate classification result of the sample to be classified may be obtained based on the automatic identification of the sample image of the sample to be classified. In the embodiment of the present application, the candidate classification results of the sample to be classified are displayed in the peripheral area of the sample image of the sample to be classified. Here, the peripheral area of the sample image can be understood as: taking the sample image as the center The area surrounding the sample image within the distance is exemplarily described below with reference to FIG. 2 .

FIG. 2 shows an example of displaying alternative classification results for samples to be classified by the sample classification method according to an embodiment of the present application. As shown in FIG. 2, the display interface includes respective sample images of a plurality of samples to be classified. For one of the sample images S1, after receiving the classification instruction, the alternative classification results are displayed in the surrounding area of the sample image S1. Includes: basophils, neutrophils, neutrophils, smear cells, and sediment. In the example shown in FIG. 2 , the candidate classification result of the sample to be classified corresponding to the sample image S1 is displayed in a circular area centered on the sample image S1 and surrounding the sample image S1, it should be understood that this is only an example According to the teachings of the present application, displaying the alternative classification result of the sample to be classified in the surrounding area of the sample image of the sample to be classified does not have to be a circular area, as long as it is in the vicinity of the sample image, so that the user can conveniently, Choose quickly. In general, compared with the method of dragging the sample image for a long distance for classification, the method of displaying the alternative classification result of the sample to be classified in the surrounding area of the sample image of the sample to be classified provided by the present application can enable the user to quickly The final classification result of the sample to be classified is selected from the alternative classification results, which improves the efficiency of sample classification, and the user operation is simple, reducing the possibility of classification errors.

In the embodiment of the present application, the alternative classification results of the samples to be classified may be displayed on the second display interface. As mentioned above, the first display interface displaying the sample images of the samples to be classified and the display interface displaying the samples to be classified The second display interface of the alternative classification result may be a different display interface, or may be a different state of the same display interface.

In an embodiment of the present application, the candidate classification results of the samples to be classified displayed in the peripheral area of the sample image may include at least one classification result obtained by classifying the samples to be classified corresponding to the sample image, and each candidate classification result may be Displayed in order based on their respective classification confidence. The classification confidence of a classification result refers to the probability that the real classification result of the sample to be classified is the classification result. For example, after classifying a sample image of a white blood cell, one of the obtained alternative classification results is a neutrophil, and the classification confidence is 90%, which means that the probability of the white blood cell being a neutrophil is 90% . In this embodiment, the candidate classification results are sorted and displayed according to their respective classification confidences. Since the classification confidence of a classification result is higher, it means that the classification result is more likely to be a real classification result. Therefore, based on the classification confidence The way of sorting and displaying can make the user find the final classification result of the samples to be classified more quickly, and improve the classification efficiency.

In one example, the higher the classification confidence is, the closer the display position of the corresponding classification result is to the sample image, which can further improve the speed of the user finding and selecting the final classification result of the sample to be classified. In another example, the candidate classification results of the samples to be classified displayed in the peripheral area of the sample image may include at least two-level classification results, wherein the classification confidence of each of the previous-level classification results is higher than that of the latter-level classification results The classification confidence of each of the results can be displayed step by step based on a user instruction, so as to obtain a final classification result of the sample to be classified based on at least one classification result. This example is described below in conjunction with Figures 3A and 3B.

3A and 3B show another example of displaying alternative classification results for samples to be classified by the sample classification method according to an embodiment of the present application, wherein FIG. 3A shows the first-level classification results of the samples to be classified, and FIG. 3B shows The second-level classification results of the samples to be classified are obtained. In this example, instead of displaying all the candidate classification results of the sample to be classified at one time, it is displayed based on a hierarchical menu, wherein the first-level classification results display the first classification results with the highest classification confidence, as shown in Figure 3A Eosinophilic, basophilic, neutrophilic, lymphatic, mononuclear; if the user believes that there is no real classification result of the sample to be classified in the first-level classification results or cannot determine whether there is a real classification of the sample to be classified in the first-level classification structure As a result, the pop-up of the second-level classification results can be triggered, and the second-level classification results show several classification results with the next highest classification confidence, as shown in Figure 3B, primitive, neutral late juvenile, atypical lymphoid, and young monocyte. It should be understood that this is only exemplary, and in practical applications, the displayed alternative classification results may include more than two-level classification results.

In the embodiment of the present application, when the user triggers the display of the next-level classification results, the user can execute a hovering instruction, a long-pressing instruction, or a clicking instruction, etc. on any one of the previous-level classification results. Alternatively, an "Other" option may be provided in the previous-level classification result, so that the user can trigger the next-level classification result through the "Other" option, as shown in the "Other" option in FIG. 3A . In the embodiment of the present application, the alternative classification result may further include an "unclear classification" option, as shown in FIG. 3B , this option may be used in a scenario where the user cannot determine the final classification result. The hovering instruction includes hovering the pointer on the preset area of the selected object to select the selection method of the object.

In the embodiment of the present application, the previous-level classification result may be closer to the sample image than the latter-level classification result, for example, the latter-level classification result may be displayed in the surrounding area of the former-level classification result, for example, as shown in FIG. 3B Yes, the second-level classification results are displayed in the surrounding area of the first-level classification results. This way of display is clear and convenient for users to make more flexible choices, such as selecting from the second-level classification results and returning to the first-level classification results. . Of course, this is only an example, and the previous-level classification result may not be displayed when the subsequent-level classification result is displayed, and an option for returning to the previous-level classification result may be provided at the same time.

In one example, the first-level classification results in the aforementioned at least two-level classification results can be displayed in at least a part of the area surrounded by the first circumference, and for the remaining level-level classification results in the at least two-level classification results, the latter level The classification result is displayed in at least a part of the area surrounded by the second circumference around the previous classification result, the first circumference is concentric with the second circumference and the radius of the second circumference is larger than that of the first circumference, for example, as shown in FIG. 3B . In this example, the classification results are displayed step by step in the form of a circle. The circle shape has a high area utilization rate, is convenient for classification, and is visually neat, so that the user can visually select the final classification result of the sample to be classified, which is convenient, fast and accurate. choice.

In general, the aforementioned example of hierarchically displaying the classification results based on the classification confidence allows the user to find the final classification result in some less candidate classification results without selecting the final classification result of the sample to be classified from a large number of options at one time. Further improve the classification efficiency.

In a further embodiment of the present application, regardless of whether the classification is displayed or not, in the embodiment in which the alternative classification results are displayed based on the classification confidence, the classification results with the highest classification confidence can be further displayed in the following manner to distinguish them from other classifications. The display method of the classification results of the confidence level is displayed, or, the classification results of different classification confidence levels are displayed separately. Exemplarily, the distinguishing display includes at least one of the following: displaying the classification results of different classification confidences with icons of different colors; displaying the classification results of different classification confidences with icons of different sizes; The icons at different distances of the image show the classification results of different classification confidences; display the classification results of different classification confidences in the text of different attributes; display the classification results of different classification confidences in areas with different shapes; The numerical value of the degree is displayed on the corresponding classification result. In this embodiment, the classification result with the highest classification confidence is displayed in a display manner that is distinguished from the classification results of other classification confidences, or the classification results with different classification confidences are displayed in a different manner from each other, which can further strengthen the user's vision. As a result, the user can more clearly see the classification results with high classification confidence, which can not only further improve the classification efficiency, but also further reduce the possibility of classification errors due to such a differentiated display.

In yet another embodiment of the present application, the alternative classification results of the samples to be classified displayed in step S130 may include at least two-level classification results, wherein the at least one-level classification results are divided into multiple groups, and the multiple groups include All possible classification results in this level of classification are listed, and the classification items included in any grouping in the previous level of classification results are the latter-level classification results, and the display of the alternative classification results includes: displaying each level by level based on user instructions. The first-level classification result is used to obtain the final classification result of the sample to be classified based on the at least one-level classification result. In this embodiment, instead of displaying all the candidate classification results of the samples to be classified at one time, it is displayed based on a hierarchical menu, wherein the first-level classification results display the grouping results of the samples to be classified, and the second-level classification results display the first-level classification results. The subordinate result of any group in the classification result, that is, the group or classification result further included in the group, and so on. This example is described below in conjunction with Figures 4A and 4B.

4A and 4B show another example of displaying alternative classification results for samples to be classified by the sample classification method according to an embodiment of the present application, wherein FIG. 4A shows the first-level classification results of the samples to be classified, and FIG. 4B shows The second-level classification results of the samples to be classified are obtained. In this example, the first-level classification results show the categories of groups to which the samples to be classified may belong, such as eosinophilic, basophilic, neutrophilic, mononuclear, and lymphatic as shown in Figure 4A; the user can Based on the first-level classification result, the group to which the sample to be classified belongs is selected as the final classification result of the to-be-classified sample, or the second-level classification result can be triggered to accurately select the next-level category of a certain group in the first-level classification result. , as shown in Figure 4B, primitive, neutral late-juvenile, atypical lymphoid, juvenile mononucleus. It should be understood that this is only exemplary, and in practical applications, the displayed alternative classification results may include more than two-level classification results.

Similar to the previous embodiment, in this embodiment, when triggering the display of the next-level classification results, the user can execute a hovering instruction, a long-pressing instruction, or a clicking instruction, etc. on any one of the previous-level classification results. Alternatively, an "other" option can be provided in the previous classification result, so that the user can trigger the subsequent classification result through the "other" option, as shown in the "other group" option in FIG. 4A . In the embodiment of the present application, the alternative classification result may further include an option of "unknown classification", as shown in FIG. 4B , this option may be used in a scenario where the user cannot determine the final classification result.

In the embodiment of the present application, the previous-level classification result may be closer to the sample image than the latter-level classification result, and the latter-level classification result may be displayed in the surrounding area of the previous-level classification result, for example, as shown in FIG. 4B , the second-level classification results are displayed in the surrounding area of the first-level classification results. This display mode is clearly structured and facilitates more flexible choices for users, such as selecting from the second-level classification results and returning to the first-level classification results. Of course, this is only an example, and the previous-level classification result may not be displayed when the subsequent-level classification result is displayed, and an option for returning to the previous-level classification result may be provided at the same time.

In one example, the first-level classification results in the aforementioned at least two-level classification results can be displayed in at least a part of the area surrounded by the first circumference, and for the remaining level-level classification results in the at least two-level classification results, the latter level The classification result is displayed in at least a part of the area surrounded by the second circumference around the previous classification result. The first circumference is concentric with the second circumference and the radius of the second circumference is larger than that of the first circumference, for example, as shown in FIG. 4B . In this example, the classification results are displayed step by step in the form of a circle. The circle shape has a high area utilization rate, is convenient for classification, and is visually neat, so that the user can visually select the final classification result of the sample to be classified, which is convenient, fast and accurate. choice.

In a further embodiment of the present application, the classification items included in each level of classification results in the at least two-level classification results in the alternative classification results described in conjunction with FIG. 4A and FIG. 4B may also be classified according to the level of classification confidence. differentiated display. In this embodiment, each classification item included in each level of classification result represents a possible grouping result or classification result of the sample to be classified, and the result also has a confidence level, and each classification item is distinguished from each other based on the confidence level and displayed. It can further strengthen the user's visual effect and improve the efficiency of the user's selection of classification results.

In general, the foregoing examples of classification results based on classification confidence and/or grouping can improve the efficiency of user information screening. Only when the previous level does not provide the target classification result, the next level, or the previous The first level provides guidance information for the latter level, so that users do not need to screen a large amount of information at the same time, so that the classification efficiency can be further improved.

In other embodiments of the present application, the classification instruction received in step S120 may further include an instruction to input a classification keyword; in this embodiment, the alternative classification results of the samples to be classified displayed in step S130 may include Classification results for keywords. In this embodiment, the corresponding candidate classification results are displayed according to the classification keywords input by the user, and the number of displayed candidate classification results can also be reduced, so that the user can quickly select the final classification results of the samples to be classified.

In a further embodiment of the present application, the alternative classification results of the samples to be classified displayed in step S130 may also include classification results obtained based on historical data, where the historical data includes final classification results of previous samples. In this embodiment, the user's selection of the final classification result of the previous sample is used as historical data to classify the current sample, so the current sample can be classified in combination with the user's historical operations, which can further improve the displayed alternative classification results It is the possibility of correct classification results (true classification results), thereby further improving the classification efficiency. Exemplarily, compared with the aforementioned classification results based on the classification confidence ranking and displayed, the display position of the classification results obtained based on historical data here can be closer to the sample image of the sample to be classified; The above classification results based on the classification confidence ranking are displayed in a display position closer to the sample image of the sample to be classified.

Referring now back to FIG. 1 , the subsequent steps of the sample classification method 100 according to an embodiment of the present application are described.

In step S140, a user's selection instruction for the candidate classification result is received, and a final classification result of the sample to be classified is acquired based on the selection instruction.

In the embodiment of the present application, based on the alternative classification results of the samples to be classified displayed in step S130, the user can select the final classification results of the samples to be classified from them through a selection instruction. Exemplarily, the selection instruction may include a long-press instruction, a click instruction, and the like on any of the alternative classification results. Based on the user's selection instruction, the final classification result of the sample to be classified can be obtained. In one example, the final classification results of the samples to be classified can be displayed.

In a further embodiment of the present application, the method 100 may further include (not shown): after obtaining the final classification result of the current sample to be classified, performing the following operations on the remaining samples located in the same initial group as the current sample to be classified Any of: automatically display the alternative classification results of the remaining samples of the same initial grouping; generate the final classification results of the remaining samples of the same initial grouping based on the final classification results of the current samples to be classified; wherein, the initial grouping It is generated by grouping based on the initial classification result of the sample. The current sample to be classified is the sample to be reclassified. The reclassification is the verification or correction of the initial classification result, and the sample is obtained after reclassification. The reclassification result is the final classification result.

In this embodiment, the sample classification method 100 of the present application is used to reclassify the samples, that is, the samples have been initially classified before the reclassification, and multiple samples may be initially classified into the same group. In this case, after any sample in the same group is reclassified according to the sample classification method 100 according to the embodiment of the present application, the alternative classification of other samples can be automatically started without inputting classification instructions for the sample images of other samples in the same group. For the presentation of the results, it is also possible to directly use the final classification result of a reclassified sample in the group as the final classification result of the other samples without repeating the process of the sample classification method 100 according to the embodiment of the present application for other samples. This can further improve the efficiency of multiple sample classification.

Based on the above description, according to the sample classification method of the embodiment of the present application, the candidate classification result of the sample to be classified is displayed in the surrounding area of the sample image of the sample to be classified, so that the user can quickly select the sample to be classified from the candidate classification results. The final classification result improves the efficiency of sample classification, and the user operation is simple, reducing the possibility of classification errors. In addition, according to the sample classification method according to the embodiment of the present application, the candidate classification results of the samples to be classified can also be displayed in a hierarchical manner, which can improve the efficiency of user information screening. level, or the former level provides guidance information to the latter level, so that the user does not need to screen a large amount of information at the same time, so that the classification efficiency can be further improved. In addition, according to the sample classification method of the embodiment of the present application, alternative classification results with different classification confidences of the samples to be classified can also be displayed separately, which can further strengthen the user's visual effect, so that the user can more clearly see the classification confidence. The classification results with high degree of classification can not only further improve the classification efficiency, but also further reduce the possibility of classification errors due to such a differentiated display.

The above exemplarily shows the sample classification method according to the embodiment of the present application. The sample classification device provided according to another aspect of the application will be described below with reference to FIG. 5 . FIG. 5 shows a schematic block diagram of a sample classification apparatus 500 according to an embodiment of the present application. As shown in FIG. 5 , the sample classification apparatus 500 may include a memory 510 , a processor 520 and a display 530 . The memory 510 stores programs for implementing corresponding steps in the sample classification method 100 according to the embodiment of the present application. The processor 520 is configured to run the program stored in the memory 510 to execute corresponding steps of the sample classification method 100 according to the embodiment of the present application, and the display 530 is configured to display information based on the control of the processor 520 . Those skilled in the art can understand the structure and operation of the sample classification apparatus 500 in combination with the foregoing description. For brevity, only the main operations of the processor 520 are described here, and some details above are not repeated.

In an embodiment of the present application, when the computer program is run by the processor 520, the processor 520 causes the processor 520 to perform the following steps: acquiring a sample image of the sample to be classified, and displaying it on the first display interface by the display 530 the sample image; receiving a classification instruction for classifying the to-be-classified sample corresponding to the sample image; controlling, based on the classification instruction, to display an alternative classification result of the to-be-classified sample in a peripheral area of the sample image, for the user to select the final classification result of the sample to be classified; receive the user's selection instruction on the candidate classification result, and obtain the final classification result of the sample to be classified based on the selection instruction.

In an embodiment of the present application, the candidate classification result is obtained by recognizing the sample image by the processor 520 .

In an embodiment of the present application, the candidate classification result includes at least one classification result obtained by classifying the sample to be classified corresponding to the sample image, and each of the candidate classification results is ranked based on the respective classification confidence show.

In an embodiment of the present application, the ranking display includes: the higher the classification confidence, the closer the display position of the corresponding classification result is to the sample image.

In an embodiment of the present application, the alternative classification results include at least two-level classification results, wherein the classification confidence of each of the previous-level classification results is higher than the classification confidence of each of the subsequent-level classification results The sorting and displaying include: displaying each level of classification results step by step based on a user instruction, so as to obtain a final classification result of the samples to be classified based on at least one level of classification results.

In an embodiment of the present application, when the alternative classification results are displayed on the display 530 or the classification results of any level in the alternative classification results are displayed, the classification results with the highest classification confidence are distinguished from other classifications The display method of the classification results of the confidence level is displayed, or, the classification results of different classification confidence levels are displayed separately.

In an embodiment of the present application, the differentiated display includes at least one of the following: displaying classification results of different classification confidences with icons of different colors; displaying classification results of different classification confidences with icons of different sizes; Icons at different distances relative to the sample image display the classification results of different classification confidences; display the classification results of different classification confidences with text with different attributes; display the classification results of different classification confidences in areas with different shapes; The numerical value of the classification confidence of the classification result is displayed on the corresponding classification result.

In an embodiment of the present application, the alternative classification results include at least two-level classification results, wherein the at least one-level classification results are divided into multiple groups, and the multiple groups include all possible classifications in this level of classification As a result, the classification item included in any group in the previous-level classification result is the latter-level classification result, and the display of the alternative classification results by the display 530 includes: displaying each level of classification results step by step based on user instructions, so as to use to obtain the final classification result of the sample to be classified based on the at least one-level classification result.

In an embodiment of the present application, each classification item included in each level of classification result in the at least two-level classification result is displayed according to the level of classification confidence.

In an embodiment of the present application, the latter-level classification result is displayed in a peripheral area of the former-level classification result.

In an embodiment of the present application, the first-level classification results in the at least two-level classification results are displayed in at least a part of the area surrounded by the first circumference, and for the remaining level-level classification results in the at least two-level classification results, The latter-level classification result is displayed in at least a partial area surrounded by a second circumference around the former-level classification result, the first circumference is concentric with the second circumference and the radius of the second circumference is greater than the radius of the second circumference. The radius of the first circle.

In an embodiment of the present application, the former-level classification result is closer to the sample image than the latter-level classification result.

In an embodiment of the present application, the alternative classification result is displayed on a second display interface, and the first display interface and the second display interface are different display interfaces.

In an embodiment of the present application, the candidate classification result and the sample image are displayed in different states of the first display interface.

In an embodiment of the present application, the classification instruction includes any one of the following instructions executed on the sample image: a drag instruction, a long press instruction, and a click instruction.

In an embodiment of the present application, the classification instruction includes an instruction to input a classification keyword, and the alternative classification results include classification results based on the classification keywords.

In an embodiment of the present application, the user instruction includes the classification instruction and any one of the following instructions executed on any one of the previous classification results: a hover instruction, a long press instruction, and a click instruction.

In an embodiment of the present application, the display 530 is further configured to: display the final classification result of the sample to be classified.

In an embodiment of the present application, the alternative classification results displayed for the samples to be classified further include classification results obtained based on historical data, where the historical data includes final classification results of previous samples.

In an embodiment of the present application, the display position of the classification results obtained based on historical data is closer to the sample image than the classification results displayed based on the classification confidence ranking; or relative to the classification results obtained based on historical data The classification results are sorted and displayed based on the classification confidence, and the display positions of the displayed classification results are closer to the sample image.

In an embodiment of the present application, when the computer program is run by the processor 520, the processor 520 further causes the processor 520 to perform the following steps: after obtaining the final classification result of the sample to be classified, compare the sample to be classified with the sample to be classified The remaining samples in the same initial group perform any one of the following operations: automatically display the alternative classification results of the remaining samples in the same initial group on the display 530; The final classification results of the remaining samples in the initial grouping; wherein the initial grouping is generated based on the initial classification results of the samples, the samples to be classified are the samples to be reclassified, and the reclassification is the The verification or correction of the initial classification result, the reclassification result obtained after the sample is reclassified is the final classification result.

In an embodiment of the present application, the peripheral area includes an area surrounding the sample image within a predetermined distance with the sample image as the center.

In an embodiment of the present application, the alternative classification results include at least two-level classification results, the at least two-level classification results are displayed step by step based on user instructions, and the final classification results of the samples to be classified are based on at least one-level classification results result obtained.

In addition, according to the embodiments of the present application, a storage medium is also provided, in which program instructions are stored, and when the program instructions are executed by a computer or a processor, corresponding steps of the sample classification method of the embodiments of the present application are performed. The storage medium may include, for example, a memory card for a smartphone, a storage unit for a tablet computer, a hard disk for a personal computer, a read only memory (ROM), an erasable programmable read only memory (EPROM), a portable compact disk read only memory (CD). - ROM), USB memory, or any combination of the above storage media. A computer-readable storage medium can be any combination of one or more computer-readable storage media.

In addition, according to the embodiments of the present application, a computer program is also provided, and the computer program can be stored in the cloud or on a local storage medium. When the computer program is run by a computer or a processor, it is used to execute the corresponding steps of the sample classification method of the embodiments of the present application.

An example of an application scenario of the sample classification method according to the embodiment of the present application is described below with reference to FIG. 6 . Figure 6 shows a schematic diagram of the structure of the chip reader. As shown in FIG. 6 , the reading machine is a kind of medical equipment, and the reading machine includes an objective lens 601 and a reading platform 602 arranged oppositely, wherein a glass slide 603 is placed on the reading platform 602, and the glass slide 603 is dripped with For a sample, such as a stained blood sample or a body fluid sample, the objective lens 601 is connected to the camera, so that the camera can photograph the sample on the glass slide 603 through the objective lens 601 to obtain a sample image.

For the sample image, the reading machine outputs the initial classification result of the sample corresponding to the sample image on the human-computer interaction device (not shown) by automatically recognizing the sample image. The sample classification method according to the embodiment of the present application can be used For reclassifying the initially classified samples, the user can invoke the sample classification method provided by the embodiment of the present application by operating the human-computer interaction device, and quickly select the sample classification results displayed in the surrounding area of the sample image from the alternative classification results. Efficiently select the final classification result of the samples, as described above.

Based on the above description, the sample classification method, device, and storage medium according to the embodiments of the present application display the candidate classification result of the sample to be classified in the surrounding area of the sample image of the sample to be classified, so that the user can quickly select the classification result from the candidate classification result. Select the final classification result of the sample to be classified, improve the efficiency of sample classification, and the user operation is simple, reducing the possibility of classification errors. In addition, the sample classification method, device, and storage medium according to the embodiments of the present application can also display the alternative classification results of the samples to be classified in a hierarchical manner, which can improve the efficiency of user information screening. Only when the target classification result is not provided at the previous level , the next level is expanded, or the previous level provides guidance information to the latter level, so that the user does not need to screen a large amount of information at the same time, so that the classification efficiency can be further improved. In addition, according to the sample classification method, device, and storage medium of the embodiments of the present application, alternative classification results with different classification confidences of samples to be classified can also be displayed in a differentiated manner, which can further enhance the user's visual effect, so that the user can more clearly Seeing the classification results with high classification confidence can not only further improve the classification efficiency, but also further reduce the possibility of classification errors due to such a differentiated display.

Although example embodiments have been described herein with reference to the accompanying drawings, it should be understood that the above-described example embodiments are exemplary only, and are not intended to limit the scope of the application thereto. Various changes and modifications may be made therein by those of ordinary skill in the art without departing from the scope and spirit of the present application. All such changes and modifications are intended to be included within the scope of this application as claimed in the appended claims.

Those of ordinary skill in the art can realize that the units and algorithm steps of each example described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Skilled artisans may implement the described functionality using different methods for each particular application, but such implementations should not be considered beyond the scope of this application.

In the several embodiments provided in this application, it should be understood that the disclosed apparatus and method may be implemented in other manners. For example, the apparatus embodiments described above are only illustrative. For example, the division of units is only a logical function division. In actual implementation, there may be other division methods, for example, multiple units or components may be combined or integrated. To another device, or some features can be ignored, or not implemented.

In the description provided herein, numerous specific details are set forth. It will be understood, however, that the embodiments of the present application may be practiced without these specific details. In some instances, well-known methods, structures and techniques have not been shown in detail in order not to obscure an understanding of this description.

Similarly, it is to be understood that in the description of the exemplary embodiments of the present application, various features of the present application are sometimes grouped together into a single embodiment, FIG. , or in its description. However, this method of application should not be construed as reflecting an intention that the claimed application requires more features than are expressly recited in each claim. Rather, as the corresponding claims reflect, the invention lies in the fact that the corresponding technical problem may be solved with less than all features of a single disclosed embodiment. Thus, the claims following the Detailed Description are hereby expressly incorporated into this Detailed Description, with each claim standing on its own as a separate embodiment of this application.

It will be understood by those skilled in the art that all features disclosed in this specification (including accompanying claims, abstract and drawings) and all features of any method or apparatus so disclosed may be used in any combination, except that the features are mutually exclusive. Processes or units are combined. Each feature disclosed in this specification (including accompanying claims, abstract and drawings) may be replaced by alternative features serving the same, equivalent or similar purpose, unless expressly stated otherwise.

Furthermore, those skilled in the art will appreciate that although some of the embodiments herein include certain features, but not others, included in other embodiments, that combinations of features of the different embodiments are intended to be within the scope of the present application And form different embodiments. For example, in the claims, any of the claimed embodiments may be used in any combination.

Various component embodiments of the present application may be implemented in hardware, or in software modules running on one or more processors, or in a combination thereof. Those skilled in the art should understand that a microprocessor or a digital signal processor (DSP) may be used in practice to implement some or all functions of some modules according to the embodiments of the present application. The present application can also be implemented as a program of apparatus (eg, computer programs and computer program products) for performing part or all of the methods described herein. Such a program implementing the present application may be stored on a computer-readable medium, or may be in the form of one or more signals. Such signals may be downloaded from Internet sites, or provided on carrier signals, or in any other form.

It should be noted that the above-described embodiments illustrate rather than limit the application, and alternative embodiments may be devised by those skilled in the art without departing from the scope of the appended claims. In the claims, any reference signs placed between parentheses shall not be construed as limiting the claim. The application can be implemented by means of hardware comprising several different elements and by means of a suitably programmed computer. In a unit claim enumerating several means, several of these means may be embodied by one and the same item of hardware. The use of the words first, second, and third, etc. do not denote any order. These words can be interpreted as names.

The above are only specific embodiments of the present application or descriptions of the specific embodiments, and the protection scope of the present application is not limited thereto. Or replacement should be covered within the protection scope of this application. The protection scope of the present application shall be subject to the protection scope of the claims.

Claims (47)

1. A sample classification method, characterized in that the method comprises:

   obtaining a sample image of the sample to be classified, and displaying the sample image on the first display interface;

   receiving a classification instruction for classifying the to-be-classified sample corresponding to the sample image;

   Display the candidate classification result of the sample to be classified based on the classification instruction in the surrounding area of the sample image, so that the user can select the final classification result of the sample to be classified;

   A user's selection instruction for the candidate classification result is received, and the final classification result of the sample to be classified is acquired based on the selection instruction.
2. The method of claim 1, wherein the candidate classification result is obtained based on automatic identification of the sample image.
3. The method according to claim 1, wherein the alternative classification results comprise at least one classification result obtained by classifying the samples to be classified corresponding to the sample images, and each of the alternative classification results is based on the respective classification Confidence is sorted and displayed.
4. The method according to claim 3, wherein the sorting display comprises: the higher the classification confidence is, the closer the display position of the corresponding classification result is to the sample image.
5. The method according to claim 3, wherein the alternative classification results include at least two-level classification results, wherein the classification confidence of each of the previous-level classification results is higher than that of each of the latter-level classification results A classification confidence, the ranking display includes:

   Each level of classification result is displayed step by step based on a user instruction, so as to obtain a final classification result of the sample to be classified based on at least one level of classification result.
6. The method according to any one of claims 3-5, characterized in that, when displaying the alternative classification results or displaying any level of classification results in the alternative classification results, it has the highest classification confidence The classification results of , are displayed in a display manner that is different from the classification results of other classification confidence levels, or, the classification results of different classification confidence levels are displayed separately.
7. The method according to claim 6, wherein the differentiated display comprises at least one of the following:

   The classification results of different classification confidences are displayed with icons in different colors;

   Display the classification results of different classification confidences with icons of different sizes;

   Display the classification results of different classification confidences with icons at different distances relative to the sample image;

   Display the classification results of different classification confidences in the text of different attributes;

   Display classification results with different classification confidences in regions with different shapes;

   The numerical value of the classification confidence of each classification result is displayed on the corresponding classification result.
8. The method according to claim 1, wherein the alternative classification results include at least two-level classification results, wherein the at least one-level classification results are divided into a plurality of groups, and the plurality of groups include the classification results of this level. For all possible classification results, the classification items included in any group in the previous-level classification results are the latter-level classification results, and the display of the alternative classification results includes:

   Each level of classification result is displayed step by step based on a user instruction, so as to obtain a final classification result of the sample to be classified based on at least one level of classification result.
9. The method according to claim 8, wherein each classification item included in each level of classification result in the at least two-level classification result is displayed according to the level of classification confidence.
10. The method according to claim 5 or 8, wherein the latter-level classification result is displayed in a peripheral area of the former-level classification result.
11. The method according to claim 10, wherein the first-level classification results of the at least two-level classification results are displayed in at least a part of the area surrounded by the first circumference, and the rest of the at least two-level classification results are displayed The classification result of the second level is displayed in at least a part of the area surrounded by the second circumference around the former classification result, and the first circumference is concentric with the second circumference and the second circumference is The radius is greater than the radius of the first circumference.
12. The method according to claim 5 or 8, wherein the previous-level classification result is closer to the sample image than the latter-level classification result.
13. The method according to claim 1, wherein the alternative classification result is displayed on a second display interface, and the first display interface and the second display interface are different display interfaces.
14. The method according to claim 1, wherein the candidate classification result and the sample image are displayed in different states of the first display interface.
15. The method according to claim 1, wherein the classification instruction includes any one of the following instructions executed on the sample image: a drag instruction, a long press instruction, and a click instruction.
16. The method according to claim 1 or 2, wherein the classification instruction includes an instruction for inputting a classification keyword, and the alternative classification result includes a classification result based on the classification keyword.
17. The method according to claim 5 or 8, wherein the user instruction includes the classification instruction and any one of the following instructions executed on any one of the previous classification results: a hovering instruction, a long press commands and click commands.
18. The method according to claim 1, wherein the method further comprises:

    Display the final classification result of the sample to be classified.
19. The method according to claim 3, wherein the alternative classification results displayed for the samples to be classified further include classification results obtained based on historical data, the historical data including final classification results of previous samples.
20. The method according to claim 19, wherein a display position of the classification results obtained based on historical data is closer to the sample image than the classification results displayed based on the classification confidence ranking; Based on the classification results obtained from the historical data, the display position of the classification results displayed based on the classification confidence ranking is closer to the sample image.
21. The method according to claim 18 or 19, wherein the method further comprises:

    After obtaining the final classification result of the sample to be classified, perform any one of the following operations on the remaining samples in the same initial group as the sample to be classified:

    Automatically display the alternative classification results of the remaining samples of the same initial grouping;

    generating the final classification results of the remaining samples of the same initial group based on the final classification results of the samples to be classified;

    The initial grouping is generated based on the initial classification result of the samples, the samples to be classified are samples to be reclassified, and the reclassification is the verification or correction of the initial classification result, and the The reclassification result obtained after the sample is reclassified is the final classification result.
22. The method according to claim 1, wherein the peripheral area comprises an area surrounding the sample image within a predetermined distance with the sample image as the center.
23. The method according to claim 1, wherein the alternative classification results include at least two-level classification results, the at least two-level classification results are displayed step by step based on user instructions, and the final classification results of the samples to be classified are based on at least one classification result.
24. A sample classification device, characterized in that the device comprises a memory, a processor and a display, wherein the memory is used for storing a program and a sample image of a sample to be classified, and the display is used for control based on the processor for display, the processor is configured to run a program in the memory to perform the following steps:

    acquiring a sample image of the sample to be classified, and displaying the sample image on the first display interface by the display;

    receiving a classification instruction for classifying the to-be-classified sample corresponding to the sample image;

    Controlling, based on the classification instruction, to display the candidate classification results of the samples to be classified in the surrounding area of the sample image, so that the user can select the final classification results of the samples to be classified;

    A user's selection instruction for the candidate classification result is received, and the final classification result of the sample to be classified is acquired based on the selection instruction.
25. The apparatus according to claim 24, wherein the candidate classification result is obtained by recognizing the sample image by the processor.
26. The apparatus according to claim 24, wherein the alternative classification results comprise at least one classification result obtained by classifying the samples to be classified corresponding to the sample images, and each of the alternative classification results is based on the respective classification Confidence is sorted and displayed.
27. The apparatus according to claim 26, wherein the ranking display comprises: the higher the classification confidence, the closer the display position of the corresponding classification result is to the sample image.
28. 27. The apparatus of claim 26, wherein the alternative classification results include at least two levels of classification results, wherein each of the previous level of classification results has a higher classification confidence than each of the latter level of classification results A classification confidence, the ranking display includes:

    Each level of classification result is displayed step by step based on a user instruction, so as to obtain a final classification result of the sample to be classified based on at least one level of classification result.
29. The device according to any one of claims 26-28, characterized in that, when displaying the alternative classification results or displaying any level of classification results in the alternative classification results on the display, there is a The classification result of the highest classification confidence is displayed in a display manner that is distinguished from the classification results of other classification confidences, or the classification results of different classification confidences are displayed separately.
30. The device according to claim 29, wherein the differentiated display comprises at least one of the following:

    The classification results of different classification confidences are displayed with icons in different colors;

    Display the classification results of different classification confidences with icons of different sizes;

    Display the classification results of different classification confidences with icons at different distances relative to the sample image;

    Display the classification results of different classification confidences in the text of different attributes;

    Display classification results with different classification confidences in regions with different shapes;

    The numerical value of the classification confidence of each classification result is displayed on the corresponding classification result.
31. 25. The apparatus of claim 24, wherein the alternative classification results include at least two-level classification results, wherein the at least one-level classification results are divided into a plurality of groups, the plurality of groups including the classification results of this level For all possible classification results, the classification item included in any group in the previous-level classification result is the latter-level classification result, and the display of the alternative classification result by the display includes:

    Each level of classification result is displayed step by step based on a user instruction, so as to obtain a final classification result of the sample to be classified based on at least one level of classification result.
32. The device according to claim 31, wherein each classification item included in each level of classification result in the at least two-level classification result is displayed according to the level of classification confidence.
33. The device according to claim 28 or 31, characterized in that, the latter-level classification result is displayed in a peripheral area of the former-level classification result.
34. The device according to claim 33, wherein a first-level classification result in the at least two-level classification results is displayed in at least a part of the area surrounded by the first circumference, and for the rest of the at least two-level classification results The classification result of the second level is displayed in at least a part of the area surrounded by the second circumference around the former classification result, and the first circumference is concentric with the second circumference and the second circumference is adjacent to the second circumference. The radius is greater than the radius of the first circumference.
35. The apparatus according to claim 28 or 31, wherein the previous-level classification result is closer to the sample image than the latter-level classification result.
36. The apparatus according to claim 24, wherein the alternative classification result is displayed on a second display interface, and the first display interface and the second display interface are different display interfaces.
37. The apparatus according to claim 24, wherein the candidate classification result and the sample image are displayed in different states of the first display interface.
38. The apparatus according to claim 24, wherein the classification instruction comprises any one of the following instructions executed on the sample image: a drag instruction, a long press instruction, and a click instruction.
39. The apparatus according to claim 24 or 25, wherein the classification instruction includes an instruction to input a classification key, and the alternative classification result includes a classification result based on the classification key.
40. The device according to claim 28 or 31, wherein the user instruction includes the classification instruction and any one of the following instructions executed on any one of the previous classification results: a hovering instruction, a long press commands and click commands.
41. The apparatus of claim 24, wherein the display is further configured to:

    Display the final classification result of the sample to be classified.
42. The apparatus according to claim 26, wherein the alternative classification results displayed for the samples to be classified further include classification results obtained based on historical data, the historical data including final classification results of previous samples.
43. The apparatus according to claim 42, wherein, compared with the classification results displayed based on the classification confidence, the display position of the classification results obtained based on historical data is closer to the sample image; For the classification results obtained from the historical data, the displayed position of the classification results sorted and displayed based on the classification confidence is closer to the sample image.
44. The apparatus according to claim 41 or 42, wherein the processor is further configured to:

    After obtaining the final classification result of the sample to be classified, perform any one of the following operations on the remaining samples in the same initial group as the sample to be classified:

    automatically displaying on the display alternative classification results for the remaining samples of the same initial grouping;

    generating the final classification results of the remaining samples of the same initial group based on the final classification results of the samples to be classified;

    The initial grouping is generated based on the initial classification result of the samples, the samples to be classified are samples to be reclassified, and the reclassification is the verification or correction of the initial classification result, and the The reclassification result obtained after the sample is reclassified is the final classification result.
45. 25. The apparatus of claim 24, wherein the peripheral area includes an area surrounding the sample image within a predetermined distance with the sample image as the center.
46. The apparatus according to claim 24, wherein the alternative classification results include at least two-level classification results, the at least two-level classification results are displayed step by step based on user instructions, and the final classification results of the samples to be classified are based on at least one classification result.
47. A storage medium, characterized in that a computer program is stored on the storage medium, and the computer program executes the sample classification method according to any one of claims 1-23 when running.

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