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Digital pathology scans glass slides into gigapixel images that can be viewed, shared and analysed by software, which is what makes AI on pathology possible.
Digital pathology manages and analyses information from digitised specimen slides, using virtual microscopy to view, share and analyse slides on screens, with applications in diagnosis and AI (Wikipedia). A whole-slide image is typically 100,000 pixels across at 40x, stored as a pyramid in vendor formats such as Aperio .svs; OpenSlide reads them vendor-neutrally. Slides are far too large for a neural network at once, so they are cut into tiles, encoded and pooled.
Showing the technology this term belongs to: Whole-slide scanners and image management.
Shares Clinical text: EHR notes and pathology reports, Cancer AI vocabulary (CanSim terms map) and the tag cansim-terms.
Shares Array and table formats: HDF5, Zarr, OME-Zarr, Parquet, Cancer AI vocabulary (CanSim terms map) and the tag cansim-terms.
Shares Array and table formats: HDF5, Zarr, OME-Zarr, Parquet, 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 Magnification (20x, 40x) and microns per pixel, 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 Cancer AI vocabulary (CanSim terms map) and the tag cansim-terms.
Shares Cancer AI vocabulary (CanSim terms map) and the tag cansim-terms.