Editing Pathology Ai (section)

== <span style="color: #FFFFFF;">Remembering</span> ==
* '''Whole Slide Image (WSI)''' — A digitized pathology slide; gigapixel images (~100,000 × 100,000 pixels) scanned at 20–40× magnification.
* '''H&E staining''' — Hematoxylin and Eosin; the standard pathology stain coloring nuclei blue and cytoplasm pink.
* '''IHC (Immunohistochemistry)''' — Staining technique detecting specific proteins; used for biomarker testing (HER2, PD-L1, ER/PR).
* '''Tumor grading''' — Assessing tumor aggressiveness from histological features; e.g., Gleason score (prostate), Bloom-Richardson (breast).
* '''Multiple Instance Learning (MIL)''' — A weakly-supervised framework handling gigapixel WSI by treating each slide as a bag of smaller patches.
* '''Patch-based classification''' — Dividing WSI into tiles (e.g., 256×256 pixels) and classifying each; used for training with slide-level labels.
* '''CLAM (Clustering-constrained Attention Multiple Instance Learning)''' — A widely used MIL framework for WSI classification.
* '''Attention mechanism (pathology)''' — Identifies which patches are most diagnostically relevant within a slide.
* '''PathAI''' — A commercial computational pathology company with FDA-cleared tools; founded by Andrew Beck.
* '''Paige''' — First FDA-authorized AI for prostate cancer pathology; detects cancer in prostate biopsies.
* '''Foundation models (pathology)''' — CONCH, UNI, Phikon — vision transformers pre-trained on millions of pathology images; strong feature extractors.
* '''Pan-cancer classification''' — Predicting tumor type directly from histology across multiple cancer types.
* '''Biomarker prediction from morphology''' — Predicting molecular alterations (MSI, BRCA mutation, TMB) from H&E histology without molecular testing.
* '''Cell segmentation (pathology)''' — Detecting and classifying individual cells (tumor, immune, stromal) within tissue; HoverNet, StarDist, CellViT.
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