From 21abde0feb745739aa9561b1bef2e811ce2b013c Mon Sep 17 00:00:00 2001 From: promptadmin Date: Sun, 5 Jul 2026 20:08:20 +0000 Subject: [PATCH] [upstream-sync] README.md from ai-boost/awesome-ai-for-science@99a57577 [catalogue] --- upstream/ai-boost-awesome-ai-for-science/catalogue/README.md | 5 +++-- 1 file changed, 3 insertions(+), 2 deletions(-) diff --git a/upstream/ai-boost-awesome-ai-for-science/catalogue/README.md b/upstream/ai-boost-awesome-ai-for-science/catalogue/README.md index 835d183..6e325ab 100644 --- a/upstream/ai-boost-awesome-ai-for-science/catalogue/README.md +++ b/upstream/ai-boost-awesome-ai-for-science/catalogue/README.md @@ -2,8 +2,8 @@ title: "Readme" task: "" lineage_type: import -upstream_source: https://github.com/ai-boost/awesome-ai-for-science/blob/a6511998/README.md -upstream_sha: a6511998 +upstream_source: https://github.com/ai-boost/awesome-ai-for-science/blob/99a57577/README.md +upstream_sha: 99a57577 imported_at: 2026-07-05 prompt_class: catalogue upstream_changes: accepted @@ -619,6 +619,7 @@ validated: false - [TRIDENT (2025)](https://github.com/mahmoodlab/TRIDENT) - Toolkit for large-scale whole-slide image processing supporting 22+ patch encoders (UNI, CONCH, Virchow, H-Optimus-0, etc.), slide encoders (TITAN, GigaPath, PRISM, CHIEF, Madeleine, Feather), tissue segmentation, and multi-GPU inference with end-to-end pipeline and smart resume for standardized deployment of computational pathology foundation models (Mahmood Lab, Harvard Medical School, 553+ stars) - [Feather (Mahmood Lab, ICML 2025 Spotlight)](https://github.com/mahmoodlab/MIL-Lab) - Lightweight supervised slide foundation model with 0.9M parameters pretrained on 24K whole-slide images for pan-cancer morphological classification, achieving competitive performance with much larger self-supervised models (TITAN, GigaPath) while enabling finetuning on consumer-grade GPUs; includes standardized MIL implementations and benchmarking across 15+ classification tasks (Mahmood Lab, Harvard Medical School, 153+ stars) - [PathChat (Nature Medicine 2024)](https://github.com/MahmoodLab/PathChat) - Multimodal generative AI assistant for computational pathology enabling interactive visual-language conversations over histopathology images for diagnostic reasoning, case discussion, and education, built on a Mistral-7B backbone with domain-specific fine-tuning (Mahmood Lab, Harvard Medical School, 1.2K+ stars) +- [SlideChat (CVPR 2025)](https://github.com/uni-medical/SlideChat) - First large vision-language assistant for gigapixel whole-slide pathology image understanding, released with the SlideInstruction dataset and SlideBench benchmark (uni-medical, Apache 2.0, 2025) - [HEST (NeurIPS 2024)](https://github.com/mahmoodlab/HEST) - Dataset and benchmarking framework integrating histology and spatial transcriptomics, enabling multimodal analysis of whole-slide images with matched spatial gene expression for advancing computational pathology and tissue microenvironment research (Mahmood Lab, Harvard Medical School, 411+ stars) #### Medical AI & Clinical Applications -- 2.54.0