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README.md

@@ -4,10 +4,33 @@ tags:
   - medical
   - pathology
   - tcga
+  - colpali
+  - visual-document-retrieval
 ---
 
-# Endometrial pathology serving assets
+# TCGA-UCEC endometrial pathology: page images + ColPali embeddings
 
-Read-only assets for the Endometrial Pathology Review tool: rendered
-report page images, precomputed visual-retrieval embeddings, and OCR text,
-derived from the public TCGA / GDC endometrial (UCEC) corpus.
+Read-only serving assets for an endometrial pathology report review tool,
+derived from the **public** TCGA / GDC endometrial (UCEC) corpus. All source
+data is public and de-identified; embeddings come from the open-weight ColPali
+model.
+
+## Contents
+- `<patient_filename>/page_NNN.png` -- rendered report page images at 200 DPI
+  (545 cases, 1921 pages), one folder per case.
+- `<patient_filename>.npz` -- ColPali page embeddings for that case: one
+  float16 array per page, keyed `page_000`, `page_001`, ...
+- `query_embeddings.npz` -- ColPali embeddings for the 9 CAP/ICCR field queries
+  (histologic_type, histologic_grade, myometrial_invasion_percent,
+  cervical_stromal_invasion, lymphovascular_space_invasion, margins,
+  regional_lymph_node_status, pathologic_stage, molecular_classification).
+- `ocr/TCGA_Reports.csv` -- OCR text for the reports.
+
+Page and query vectors are scored with late-interaction MaxSim (ColBERT-style)
+on CPU, no GPU needed at serve time.
+
+## Provenance
+- Page images: GDC pathology-report PDFs (files-API lookup by case barcode +
+  `data_type="Pathology Report"`), rendered to PNG at 200 DPI (PyMuPDF).
+- OCR text: tatonetti-lab/tcga-path-reports (Kefeli et al. 2024, *Patterns*).
+- Embeddings: `vidore/colpali-v1.3`, multi-vector, bf16 inference, stored float16.