Editing Computational Pathology (section)

== <span style="color: #FFFFFF;">Understanding</span> ==
Pathology AI faces a unique challenge: slides are gigapixel-scale images far too large for direct processing by neural networks (a 40× WSI can be 100,000 × 100,000 pixels = 10 billion pixels). Two dominant strategies address this:

**Patch-based approaches**: Extract thousands of smaller patches (256×256 or 512×512 pixels) from each slide. Train a CNN or ViT on each patch individually. Aggregate patch-level predictions to a slide-level diagnosis. This works but requires patch-level annotations, which are expensive and often unavailable.

**Multiple Instance Learning (MIL)**: The dominant approach for slide-level labels. Each slide is a "bag" of patches. The bag label (e.g., cancer present) is known, but which patches contain cancer is unknown. MIL aggregates patch features using attention or pooling to produce a slide-level prediction. CLAM's attention mechanism additionally identifies which patches are driving the prediction — providing weak localization.

**Pathology foundation models**: Pre-trained on millions of pathology patches using self-supervised learning (DINO, MAE, DINOv2), models like UNI, CONCH, and Prov-GigaPath learn rich histological feature representations. These serve as feature extractors for downstream tasks with minimal labeled data — a major advance for data-scarce pathology problems.

**Biomarker prediction from morphology**: Neural networks trained on paired (WSI, molecular test result) data can predict molecular biomarkers from histology alone. TCGA-trained models predict microsatellite instability (MSI), BRAF mutation, HER2 amplification, and survival from H&E slides without any molecular testing. These predictions are not yet clinical-grade but suggest deep morphological correlates of molecular biology.

**FDA-cleared pathology AI**: Paige Prostate is the first FDA-authorized AI for prostate cancer detection. PathAI and other companies have cleared tools for various cancer types. Regulatory scrutiny is high: prospective clinical validation, algorithmic bias testing, and reader studies are required.
</div>

<div style="background-color: #8B0000; color: #FFFFFF; padding: 20px; border-radius: 8px; margin-bottom: 15px;">