A large-scale (194k), Multiple-Choice Question Answering (MCQA) dataset designed to address realworld medical entrance exam questions.
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Updated
Nov 28, 2022 - Jupyter Notebook
A large-scale (194k), Multiple-Choice Question Answering (MCQA) dataset designed to address realworld medical entrance exam questions.
Machine reading comprehension on clinical case reports
[NeurIPS 2023 Oral] Quilt-1M: One Million Image-Text Pairs for Histopathology.
中文医疗问诊大模型MedChatZH,具有中西医问诊、优秀的对话能力
HEAD-QA: A Healthcare Dataset for Complex Reasoning
Predicting multigraph brain population from a single graph
paper list, dataset, and tools for radiology report generation
Dental Caries Degree Detection based on Fuzzy Cognitive Maps and Genetic Algorithm
Repository for the paper 'MDS-ED: Multimodal Decision Support in the Emergency Department – a benchmark dataset based on MIMIC-IV'.
Fake-Heart-Sensor-Data-Using-Python-and-Kafka is a GitHub project that provides a simple and easy-to-use way to generate simulated heart sensor data using Python and Kafka. This project is ideal for developers who want to test their applications with realistic heart sensor data or simulate a data stream for research purposes.
CLinical Information Retrieval Evaluation Collection
Medical Report Generation And VQA (Adapting XrayGPT to Any Modality)
This repository explores the use of advanced sequence-to-sequence networks and transformer models, such as BERT, BART, PEGASUS, and T5, for summarizing multi-text documents in the medical domain. It leverages extensive datasets like CORD-19 and a Biomedical Abstracts dataset from Hugging Face to fine-tune these models.
Building our own naïveBayes classifier to predict categories for future queries.
Understanding Cellgen's dataset.
We use a tabular dataset which contains health information of patients to predict whether they suffer from a heart disease. Two notebooks are present currently in the repo, one focuses on data preprocessing, exploration and visualisation, while the other focuses on model creation, training and evaluation.
Logistic Regression Classification Model using socioeconomic and medical factors to categorize stroke status.
siim-medical-image-analysis-tutorial from Kaggle
Project on Bayesian Networks did during my master in AI
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