Imported from VectorSpaceLab/AREX-Skill (skills/repositories/repo-skills/clam/sub-skills/heatmap-visualization/SKILL.md). Install upstream with npx skills add VectorSpaceLab/AREX-Skill --skill heatmap-visualization. Copyright stays with the author (GPL 3.0).

CLAM Heatmap Visualization

Use this sub-skill when the task is to create CLAM attention heatmaps, validate a heatmap YAML/process list, configure ROI or top-k patch sampling, explain create_heatmaps.py outputs, or diagnose heatmap failures caused by checkpoint, encoder, model, or slide-input mismatches.

Route Tasks

• For heatmap YAML structure, process-list columns, model/encoder consistency, and safe preflight checks, read references/configuration.md and run scripts/validate_heatmap_config.py.
• For end-to-end heatmap commands, the interactive Continue? prompt, raw versus production outputs, ROI rendering, and sampled-patch outputs, read references/workflows.md.
• For checkpoint shape errors, missing UNI/CONCH settings, absent slides, ROI column problems, GPU memory issues, and heatmap rendering options, read references/troubleshooting.md.
• For patch-coordinate generation before heatmaps, use ../wsi-preprocessing/SKILL.md.
• For pretrained encoder setup and feature-dimension choices, use ../feature-extraction/SKILL.md.
• For training checkpoints and evaluation-time model arguments, use ../training-evaluation/SKILL.md.

Key CLAM Facts

• CLAM heatmaps are produced by create_heatmaps.py --config_file <yaml-name>, where the script resolves the config under heatmaps/configs/.
• Heatmap inference initializes a CLAM checkpoint, loads a pretrained encoder, scans slides or ROI regions with OpenSlide, computes attention scores, and writes both raw HDF5 assets and rendered production images.
• ResNet50-truncated and UNI features are 1024-dimensional; CONCH v1 features are 512-dimensional, so model_arguments.embed_dim must match the checkpoint and encoder family.
• The bundled validator is static and safe: it checks YAML/CSV shape and consistency without loading checkpoints, WSI files, OpenSlide, PyTorch, or encoders.