The BreastScreening project is a collaboration between Instituto Superior Técnico, University of Adelaide and University of Queensland. Also, the BreastScreening project receives contributions from both MIDA and MIMBCD-UI projects.
With the BreastScreening project we have two research questions: 1) can we use Deep ConvNet models pre-trained from Computer Vision (CV) datasets on medical image analysis applications; and 2) can we use Deep ConvNet to analyse unregistered medical images.
We present a novel methodology for the automated detection of breast lesions from Dynamic Contrast-Enhanced Magnetic Resonance (DCE-MRI) volumes. Our method, based on Deep Reinforcement Learning (DRL), significantly reduces the inference time for lesion detection compared to an exhaustive search, while retaining state-of-art accuracy.
In short, the BreastScreening project is an automated analysis of Multi-Modal Medical Data using Deep Belief Networks (DBN). This project is an ARC Discovery Project (DP140102794) aiming at: (i) automatically detect and segment suspicious regions from different breast imaging data, e.g., MG, US and MRI; (ii) estimate BI-RADS scores (from 0 to 6) from the segmentations and the patient’s clinical records; (iii) retrieve similar cases from our database, given the above estimation results; and (iv) automatically extract relevant features from any combination of input data types.
The following information shows our resources and acknowledgements across the project development. It is in this section, where we link our datasets and important people, as well as projects to our research.
During the development of this project we generated a combination of interesting datasets. To publish our related datasets we used a well known platform called Kaggle. To access our project's Profile Page just follow the link.
A special thanks to Chris Hafey, the propelling person of CornerstoneJS, who also developed the cornerstoneDemo. Not forgetting the three supporters of the CornerstoneJS library, Aloïs Dreyfus, Danny Brown and Erik Ziegler. We also would like to give a special thanks to Erik Ziegler who support several issues during this path. Thanks also Pedro Miraldo, Carlos Santiago, Bruno Cardoso and Bruno Oliveira for the technical help given.
