mahmoodlab/CLAM

Health v2 · maintenance only

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Adoption not part of the score

1728 stars · 517 forks observed · 2026-08-28

What it is AI-extracted, prompt v1, taxonomy v1, 2026-08-30, confidence not recorded

CLAM is an open-source Python toolkit for data-efficient, weakly supervised classification of whole-slide images (WSIs) in computational pathology. It converts slide-level labels into interpretable slide-level classifiers without ROI extraction or patch-level annotations, using attention-based multiple-instance learning.

Use cases

• classify whole-slide pathology images with only slide-level labels
• subtype renal cell carcinoma or lung cancer from WSIs
• detect lymph node metastasis in breast cancer slides
• visualize attention heatmaps over histology slides
• train models on TCGA or Camelyon16/17 datasets
• process gigapixel WSIs into patches and embeddings

When to choose

• you need interpretable WSI classification without patch-level annotations
• you work with gigapixel histopathology slides and limited labels
• you want a published, widely cited computational pathology pipeline

When to avoid

• you need general-purpose medical imaging outside histopathology
• you require per-pixel segmentation annotations
• your project needs a permissive license (CLAM is GPL-3.0)

Facets

library · maturity active

machine-learning deep-learning image-processing computer-vision deep-learning healthcare image-processing python windows whole-slide-imaging histopathology multiple-instance-learning weakly-supervised-learning digital-pathology tcga computational-pathology linux macos

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