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 (Computers in Biology and Medchine 2024)

HEAD-QA: A Healthcare Dataset for Complex Reasoning

paper list, dataset, and tools for radiology report generation

Repository for the paper 'MDS-ED: Multimodal Decision Support in the Emergency Department – a benchmark dataset based on MIMIC-IV'.

Predicting multigraph brain population from a single graph

Dental Caries Degree Detection based on Fuzzy Cognitive Maps and Genetic Algorithm

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.

Get started building safe, reliable, and production-grade multi-agent systems for medical applications!

CLinical Information Retrieval Evaluation Collection

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.

Medical Report Generation And VQA (Adapting XrayGPT to Any Modality)

Understanding Cellgen's dataset.

Building our own naïveBayes classifier to predict categories for future queries.

Logistic Regression Classification Model using socioeconomic and medical factors to categorize stroke status.

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.

Project on Bayesian Networks did during my master in AI

siim-medical-image-analysis-tutorial from Kaggle