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name: clam description: "Use CLAM for weakly supervised computational pathology workflows on whole-slide images, from WSI patching through features, training, evaluation, and heatmaps." disable-model-invocation: true metadata: disco-role: operating license: GPL 3.0

CLAM

Use this repo skill when a task involves CLAM: data-efficient, weakly supervised computational pathology on whole-slide images with slide-level labels. CLAM workflows usually move from WSI segmentation and patch coordinates, to feature extraction, to CLAM/MIL training and evaluation, and finally to attention heatmap visualization.

Route First

• For raw WSIs, tissue masks, patch-coordinate .h5 files, stitched QC images, process lists, or segmentation presets, read sub-skills/wsi-preprocessing/SKILL.md.
• For ResNet50, UNI, or CONCH feature extraction into h5_files/ and pt_files/, read sub-skills/feature-extraction/SKILL.md.
• For dataset CSVs, train/validation/test splits, main.py, eval.py, CLAM_SB/CLAM_MB/MIL choices, checkpoints, or metrics, read sub-skills/training-evaluation/SKILL.md.
• For attention heatmaps, create_heatmaps.py, YAML/process-list validation, ROI heatmaps, sampled patches, or checkpoint visualization, read sub-skills/heatmap-visualization/SKILL.md.
• For install/import, OpenSlide, optional encoder checkpoints, GPU/runtime, or cross-workflow failures, start with references/troubleshooting.md.

Pipeline Order

1. Prepare WSIs with the fast coordinate pipeline: segment tissue, generate patches/<slide>.h5, and optionally create masks/stitches for QC.
2. Extract patch features from those coordinate bags and original slides into feature h5_files/ and pt_files/.
3. Prepare dataset CSVs and splits, then train or evaluate CLAM/MIL models against the feature root.
4. Use trained checkpoints plus heatmap YAML configuration to render attention heatmaps and sampled high-attention patches.

Key CLAM Facts

• CLAM is script-oriented rather than an installable Python package; public workflows run repository scripts such as create_patches_fp.py, extract_features_fp.py, main.py, eval.py, and create_heatmaps.py from a CLAM working tree or equivalent copy.
• The documented environment targets Python 3.10 with OpenSlide, PyTorch, torchvision, timm, h5py, pandas, PyYAML, OpenCV, matplotlib, scikit-learn, scipy, tqdm, openslide-python, smooth-topk, and tensorboardX.
• resnet50_trunc is the default encoder; uni_v1 needs UNI_CKPT_PATH; conch_v1 needs the CONCH package plus CONCH_CKPT_PATH.
• ResNet50 and UNI features use --embed_dim 1024; CONCH features use --embed_dim 512 in training, evaluation, and heatmap configs.
• Heavy native runs can require WSIs, OpenSlide system libraries, GPU memory, pretrained encoder checkpoints, and trained model checkpoints. Use bundled helpers for command/config preflight before launching expensive jobs.

Public Setup Check

From a CLAM working tree, follow the upstream public setup path:

conda env create -f env.yml
conda activate clam_latest

For optional CONCH feature extraction, also install the upstream CONCH package and set CONCH_CKPT_PATH; for UNI, set UNI_CKPT_PATH to the downloaded checkpoint. After setup, run this minimal source-tree import check from the CLAM working tree before launching heavy workflows:

python -c "import torch, openslide, pandas; from models.model_clam import CLAM_SB; from dataset_modules.dataset_generic import Generic_MIL_Dataset; print('CLAM import ok')"

References

• Read references/pipeline-overview.md for a concise end-to-end map of CLAM inputs, scripts, outputs, and handoffs between sub-skills.
• Read references/troubleshooting.md for cross-cutting install/import, OpenSlide, encoder, data-layout, GPU, and script-working-directory failures.
• Read references/repo-provenance.md when deciding whether this skill is stale relative to the source repository.
• references/repo-routing-metadata.json is structured metadata used by the managed repo-skills router during import.

Bundled Scripts

• Run scripts/clam_preflight.py --help for a safe, dependency-light checklist that validates expected CLAM workflow paths and reminds agents which sub-skill owns each stage.
• Sub-skills include deeper workflow helpers for preprocessing command generation, preset CSV creation, feature extraction command generation, split/train/eval command generation, and heatmap YAML validation.

Safety Notes

• Do not tell a future user to rely on this generated skill's source checkout or artifact reports at runtime; use bundled references and scripts.
• Do not run WSI processing, feature extraction, model training, checkpoint evaluation, or heatmap generation without confirming required data, checkpoints, runtime hardware, and output locations.
• Prefer fast dry-run helpers and YAML/CSV validation before launching GPU-heavy or storage-heavy CLAM jobs.