A Curated List of Awesome Works in Computational Pathology, Aiming to Serve as a One-stop Resource for Researchers, Practitioners, and Enthusiasts Interested in Digital Pathology.
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Updated
Aug 13, 2026 - Python
A Curated List of Awesome Works in Computational Pathology, Aiming to Serve as a One-stop Resource for Researchers, Practitioners, and Enthusiasts Interested in Digital Pathology.
Officially Accepted to IEEE Transactions on Medical Imaging (TMI, IF: 11.037) - Special Issue on Geometric Deep Learning in Medical Imaging.
A curated list of foundation models, datasets, and tools for biosignals
RETFound - A foundation model for retinal image
NeurIPS'24 DB (Spotlight) | Instruction Tuning Large Language Models to Understand Electronic Health Records
Code for the KDD'26 paper "ClinicalBench: Can LLMs Beat Traditional ML Models in Clinical Prediction?"
This is the official repository for DiPro, highlighted as a Spotlight at NeurIPS 2025.
An Explainable Geometric-Weighted Graph Attention Network (xGW-GAT) for Identifying Functional Networks Associated with Gait Impairment
Demographic bias in misdiagnosis by computational pathology models - Nature Medicine
Reading list for multimodal learning in healthcare
HD3C: A lightweight classification framework designed for low-power devices.
🎓 Automatically Update AI4Science Papers by Category Daily using Github Actions
JAMIA: A Novel Generative Multi-Task Representation Learning Approach for Predicting Postoperative Complications in Cardiac Surgery Patients
Quickstart to Bioinformatics & Biomedical AI.
AI psychiatrist assistant for measuring depression symptoms from clinical interview transcripts
[BioNLP ACL'24] Gla-AI4BioMed at RRG24: Visual Instruction-tuned Adaptation for Radiology Report Generation
Repository to explain the projects currently being developed at Foundation29.
A machine learning-powered web application that predicts the risk of heart disease and muscle weakness based on user input. Built with Flask, python, and deployed on Render.
AI assistant for frontline health workers to improve maternal care, nutrition, and scheme access.
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