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A whole slide is cut into thousands of small square tiles, each tile is turned into a vector by an image model, and the vectors are pooled to describe the slide.
Multiple-instance learning is supervised learning in which the learner receives labelled bags of instances rather than individually labelled instances (Wikipedia); a slide is the bag and its tiles the instances, since the diagnosis is known for the slide but not for each square. Tiles (typically 224 to 256 pixels at 20x) are encoded by a frozen pathology foundation model such as UNI or Virchow, and an attention-based pooling (ABMIL, CLAM) weights the tiles to make a slide-level prediction while showing which regions drove it.
Showing the technology this term belongs to: Pathology & radiology foundation models.
Shares Digital pathology and whole-slide images (WSI), Cancer AI vocabulary (CanSim terms map) and the tag cansim-terms.
Shares Digital pathology and whole-slide images (WSI), Cancer AI vocabulary (CanSim terms map) and the tag cansim-terms.
Shares Attention-based multiple-instance learning (ABMIL, CLAM), Cancer AI vocabulary (CanSim terms map) and the tag cansim-terms.
Shares Magnification (20x, 40x) and microns per pixel, Cancer AI vocabulary (CanSim terms map) and the tag cansim-terms.
Shares Pathology foundation models: UNI, UNI2, Virchow2, CTransPath, CONCH, TITAN, Pathology & radiology foundation models, Cancer AI vocabulary (CanSim terms map) and the tag cansim-terms.
Shares Pathology foundation models: UNI, UNI2, Virchow2, CTransPath, CONCH, TITAN, Cancer AI vocabulary (CanSim terms map) and the tag cansim-terms.
Shares Cancer AI vocabulary (CanSim terms map) and the tag cansim-terms.
Shares Cancer AI vocabulary (CanSim terms map) and the tag cansim-terms.