Extract and normalize histology image tiles

Patch-based whole-slide image (WSI) analysis for digital pathology

Cancer histopathology or tissue classification when patch-level features matter more than whole-slide; enables GPU-scale training on high-res scans.

• · whole-slide image (.svs, .tiff)
• · tissue type
• · patch extraction parameters (size, stride, quality filter)

• · extracted image patches (PIL format)
• · patch metadata (coordinates, tissue content %)
• · curated dataset for ML training

• · OpenSlide (slide reading)
• · PIL/Pillow (patch writing)
• · numpy (filtering)

Slide file accessible; patch size ≤ available slide resolution; quality_filter threshold set

• · patches extracted from background/artifact regions → pollutes training set
• · patch coordinates not stored → reproducibility lost
• · stride too small → overlapping patches waste compute

• · OpenSlide integration documented
• · Metadata tracking (coords, tissue %) enforces reproducibility