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Slides are scanned at 20x or 40x objective magnification, about 0.5 or 0.25 microns per pixel; a model trained at one scale can misread tissue at another.
Digital pathology scans slides with virtual microscopy at a chosen objective magnification (Wikipedia). 40x resolves nuclear detail at roughly 0.25 microns per pixel and 20x, at roughly 0.5, is the usual compromise between detail and file size; scanners record the exact microns per pixel in the file header. Pathology foundation models are trained at set magnifications and tile sizes, so preprocessing must rescale slides to the training resolution, and a change of scanner is a batch effect for images.
Shares Tile and patch encoding of slides, Digital pathology and whole-slide images (WSI), Cancer AI vocabulary (CanSim terms map) and the tag cansim-terms.
Shares Tile and patch encoding of slides, 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 Batch effects and harmonisation, Cancer AI vocabulary (CanSim terms map) and the tag cansim-terms.
Shares Batch effects and harmonisation, 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 Batch effects and harmonisation, Cancer AI vocabulary (CanSim terms map) and the tag cansim-terms.
Shares Batch effects and harmonisation, Cancer AI vocabulary (CanSim terms map) and the tag cansim-terms.