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CLAM (Clustering-constrained Attention Multiple instance learning) — weakly supervised computational pathology framework for whole slide image (WSI) classification and survival analysis. Processes gig
Use with AI
Install the MCP server or CLI to instantly fetch CLAM documentation:
Install command
claude mcp add biocontext7 -- npx @biocontext7/mcpOr share this page: biocontext7.com/tools/clam
Use when processing highly multiplexed tissue images with MCMICRO, the end-to-end Nextflow pipeline for CyCIF, CODEX, mIHC, and other cyclic immunofluorescence imaging modalities. Covers illumination
1 shared topic • 2 shared operations
Use when working with MENDER (MultilayEred NeighborhooDhood-Encoded Representations), a Python tool for spatial domain identification in multiplexed imaging data. Covers spatial domain segmentation in
1 shared topic • 2 shared operations
Napari — fast, interactive, multi-dimensional image viewer for Python built on Qt and vispy (OpenGL). Provides GPU-accelerated rendering of 2D, 3D, and nD image data with seven layer types (Image, Lab
1 shared topic • 2 shared operations
Squidpy — spatial single-cell analysis toolkit in the scverse ecosystem for analyzing and visualizing spatial molecular data. Builds spatial neighbor graphs from tissue coordinates (Visium, Xenium, ME
1 shared topic • 2 shared operations
STARmap (Spatially-resolved Transcript Amplicon Readout mapping) — in situ spatial transcriptomics method that combines hydrogel-tissue chemistry with sequencing-by-hybridization (SEDAL) for multiplex
1 shared topic • 2 shared operations