MICS-Lab/thunder
[NeurIPS25 D&B Spotlight] A tile-level histopathology image understanding benchmark
This project helps computational pathology researchers and medical AI developers evaluate and compare different foundation models for analyzing histopathology images. You input various foundation models and a diverse collection of cancer histology datasets, and it outputs performance metrics related to downstream task accuracy, feature representations, robustness, and uncertainty. It is designed for those who develop or deploy AI models for digital pathology, allowing them to thoroughly assess model capabilities.
Use this if you need to rigorously compare the performance of different AI foundation models on a wide range of histopathology image analysis tasks and datasets.
Not ideal if you are a clinician looking for a diagnostic tool or a patient seeking medical advice; this is a research and development benchmark.
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43
Forks
11
Language
Python
License
CC-BY-4.0
Category
Last pushed
Feb 27, 2026
Commits (30d)
0
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