Stars
Knowledge-Guided Adaptation of Pathology Foundation Models Improves Cross-domain Generalization and Demographic Fairness
A search engine for Digital Histopathology Archives
Squeezing Scientific Creativity Out of Language Models Made Easy
GPT4All: Run Local LLMs on Any Device. Open-source and available for commercial use.
Computed tomography to body composition (Comp2Comp).
Tool for robust segmentation of >100 important anatomical structures in CT and MR images
[EARTH@MICCAI'2024] EndoGS: Deformable Endoscopic Tissues Reconstruction with Gaussian Splatting
GPT4 based personalized ArXiv paper assistant bot
FEMR (Framework for Electronic Medical Records) provides tooling for large-scale, self-supervised learning using electronic health records
The official codes for "PMC-LLaMA: Towards Building Open-source Language Models for Medicine"
[CVPR'2023] Taming Diffusion Models for Audio-Driven Co-Speech Gesture Generation
[AAAI'23] MulGT: Multi-task Graph-Transformer with Task-aware Knowledge Injection and Domain Knowledge-driven Pooling for Whole Slide Image Analysis
This is the pytorch\DGL implementation of the AMIGO paper.
[CVPR'23] Histopathology Whole Slide Image Analysis with Heterogeneous Graph Representation Learning
[NeurIPS'22] Multi-Granularity Cross-modal Alignment for Generalized Medical Visual Representation Learning
SSSegmentation: An Open Source Supervised Semantic Segmentation Toolbox Based on PyTorch.
Fusing Histology and Genomics via Deep Learning - IEEE TMI
Code for "Graph Neural Network on Electronic Health Records for Predicting Alzheimer’s Disease"
An index of algorithms for learning causality with data
[ACL-2021] The official implementation of Cross-modal Memory Networks for Radiology Report Generation.
This repository contains links to machine learning exams, homework assignments, and exercises that can help you test your understanding.
Codebase for Time-series Generative Adversarial Networks (TimeGAN) - NeurIPS 2019
Code and data accompanying Natural Language Processing with PyTorch published by O'Reilly Media https://amzn.to/3JUgR2L