Upload README.md with huggingface_hub

README.md

@@ -0,0 +1,179 @@
+---
+license: cc-by-4.0
+task_categories:
+  - image-segmentation
+tags:
+  - medical
+  - histopathology
+  - whole-slide-imaging
+  - veterinary
+  - canine
+  - dermatology
+  - oncology
+size_categories:
+  - n<1K
+---
+
+# CATCH — CAnine CuTaneous Cancer Histology
+
+350 whole-slide images of **canine** skin tumors (H&E), covering **7 tumor
+subtypes** with dense multi-class region annotations by a veterinary
+pathologist. Mirrored for the MedOtter segmentation benchmark.
+
+> Wilm F., Fragoso M., Marzahl C., Qiu J., Puget C., Diehl L., Bertram C.A.,
+> Klopfleisch R., Maier A., Breininger K.*, Aubreville M.* —
+> *Pan-tumor CAnine cuTaneous Cancer Histology (CATCH) dataset*,
+> **Scientific Data** 9, 588 (2022). doi:10.1038/s41597-022-01692-w
+>
+> Data DOI: [10.7937/TCIA.2M93-FX66](https://doi.org/10.7937/TCIA.2M93-FX66) ·
+> Source: [TCIA CATCH collection](https://www.cancerimagingarchive.net/collection/catch/)
+
+## What this mirror contains — read before using
+
+The originals are 350 Aperio `.svs` slides totalling **522 GB** at 0.2533 µm/px,
+distributed by TCIA behind an Aspera plugin. This mirror stores each slide
+rendered at the **4 µm/px pyramid level** — the exact resolution the CATCH
+paper's own segmentation baseline operates at (512×512 px ≙ 2048×2048 µm) —
+as a lossless PNG, paired with a 13-class indexed mask rasterized at the same
+level. It is a **derived, downsampled** representation, not the original WSIs.
+
+For full-resolution work, use TCIA. The complete original polygon annotations
+are included here as `CATCH.json` so any other pyramid level can be re-derived.
+
+| | |
+|---|---|
+| Slides | 350 |
+| Patients | 282 |
+| Resolution | 4.05 µm/px (pyramid level 2, ≈16× downsample, all 350 slides) |
+| Image size | median 6025×4644, max 11812×6046, mean 29.7 Mpx |
+| Polygons | 12,424 |
+| Classes | 13 (+ label 0 = unannotated) |
+| Splits | train 245 / val 35 / test 70 (official, patient-level) |
+
+## Splits
+
+The official split from the authors' [CanineCutaneousTumors](https://github.com/DeepPathology/CanineCutaneousTumors)
+repo, balanced at **35 / 5 / 10 slides per subtype**. It is patient-level:
+**no patient appears in two splits** (verified across all 282 patients).
+
+A slide's patient is the filename prefix `<Subtype>_<NN>` — 41 patients
+contribute 2 slides, 8 contribute 3, 2 contribute 4, and 1 contributes 6.
+**Group on `patient_id`, not on `slide`.**
+
+## Labels
+
+`mask` is a single-channel uint8 PNG. **Label 0 means *unannotated*, not
+background** — CATCH has no background class by design, and the authors
+exclude unannotated tissue from training and evaluation. Treat 0 as
+*don't-care*, or synthesize a background class by Otsu-thresholding the white
+point per slide, which is what the paper's baseline does.
+
+| ID | Class | Group | Slides present |
+|---|---|---|---|
+| 0 | unannotated | — | 350 |
+| 1 | Bone | Tissue | 21 |
+| 2 | Cartilage | Tissue | 4 |
+| 3 | Dermis | Tissue | 322 |
+| 4 | Epidermis | Tissue | 321 |
+| 5 | Subcutis | Tissue | 246 |
+| 6 | Inflamm/Necrosis | Tissue | 149 |
+| 7 | Melanoma | Tumor | 50 |
+| 8 | Plasmacytoma | Tumor | 50 |
+| 9 | Mast Cell Tumor | Tumor | 50 |
+| 10 | PNST | Tumor | 50 |
+| 11 | SCC | Tumor | 50 |
+| 12 | Trichoblastoma | Tumor | 50 |
+| 13 | Histiocytoma | Tumor | 50 |
+
+**Bone (21 slides) and Cartilage (4 slides) are too rare for class-averaged
+metrics** — the authors exclude both from their own baseline. Do the same.
+
+**Exactly one tumor class occurs per slide**, and it always equals the slide's
+subtype (verified 350/350). `tumor_class_id` / `tumor_class_name` give it
+directly, so a binary tumor-vs-rest target needs no lookup.
+
+### Polygons are hierarchical — rasterize in file order
+
+Annotations nest: the dermis encircles a tumor mass, and islands of normal
+dermis sit inside the tumor. Masks here are rasterized in **COCO file order**,
+which is the authors' documented sort — *"polygons are sorted in increasing
+order of their hierarchy level, i.e. polygons enclosed by another will be read
+out after their enclosing polygon"* — so a later fill correctly overwrites the
+region it sits within.
+
+⚠️ **Do not sort by the `area` field instead.** `area` is the shoelace area, so
+a polygon drawn as a *ring* around a tumor reports a **smaller** area than the
+blob it encloses, while a standard polygon fill (`cv2.fillPoly`, `PIL
+ImageDraw.polygon`) fills its outer boundary solid. Sorting area-descending
+therefore paints the ring last and **buries the tumor completely**. Measured on
+this data, it destroys the entire tumor annotation on `Plasmacytoma_08_1`,
+`Trichoblastoma_31_2` and `Trichoblastoma_34_1`.
+
+File order reproduces the per-class slide presence of the source polygons
+**exactly** for all 13 classes across all 350 slides; area-descending does not.
+
+## Columns
+
+`image` · `mask` · `slide` · `stem` · `subtype` · `patient_id` · `split` ·
+`scanner` · `tumor_class_id` · `tumor_class_name` · `width` · `height` · `mpp` ·
+`downsample` �� `level0_width` · `level0_height` · `annotated_frac` ·
+`classes_present`
+
+`annotated_frac` is the fraction of canvas carrying a label (median ≈ 0.50;
+much of the remainder is glass, not untraced tissue).
+
+## Overlap with other datasets — leakage warnings
+
+- **Multi-Scanner Canine Cutaneous SCC** ([Zenodo 7418555](https://zenodo.org/records/7418555))
+  re-scans **44 of CATCH's 50 SCC slides** on 4 additional scanners. Confirmed
+  by the authors and **joinable by filename**. Do not mix.
+- **MIDOG++ / MIDOG 2022 Domain 4** is 50 canine cutaneous mast cell tumor
+  cases from the same archive, scanner and resolution as CATCH's 50 MCT
+  slides. The reuse is undocumented and **no cross-reference ID exists** — the
+  naming schemes are not joinable. Treat the MCT subset as potentially
+  contaminated if you also use MIDOG.
+- **CCMCT / MITOS_WSI_CCMCT** (32 canine cutaneous MCT WSIs) may likewise
+  overlap the MCT subset. Also unjoinable.
+- **No overlap with human histopathology sets** (TCGA-derived, PanNuke,
+  MoNuSeg, MoNuSAC, NuCLS, CoNIC, CAMELYON, …) — different species.
+
+⚠️ Web summaries claiming "CATCH is on Zenodo as 4 µm/px TIFFs" are **wrong**;
+that record is the 44-slide Multi-Scanner SCC derivative, not CATCH.
+
+## Annotation provenance
+
+One annotation tier is released. Pathologist **M. Fragoso** drew ~82% of the
+annotations; the remainder was drawn by three medical students and then
+reviewed for correctness and completeness by M. Fragoso. The distributed
+SQLite has exactly one entry in its `Persons` table — a single merged layer,
+with no algorithmic pre-annotation. Two further veterinary pathologists
+annotated one ROI on each of the 70 test slides for an inter-rater study;
+**that data was never published** and is not part of this dataset.
+
+Reported reliability (paper Table 3, generalized conformity index): tumor
+0.8514, epidermis 0.7512. Dermis/subcutis are the weakest pair, and
+inflammation/necrosis vs tumor is the other main confusion axis.
+
+## License
+
+**CC BY 4.0**, as stated for all three data rows on the
+[TCIA collection page](https://www.cancerimagingarchive.net/collection/catch/),
+the authors' designated distribution channel.
+
+⚠️ **Discrepancy, disclosed for transparency:** the `licenses` block *inside*
+the official `CATCH.json` declares `Attribution-NonCommercial-NoDerivs 2.0`.
+This appears to be a COCO-export template default rather than a deliberate
+choice, and it is contradicted by TCIA's own Data Access table and by the
+CC BY 4.0 paper. We treat the TCIA statement as controlling. If your use is
+commercial or derivative-heavy, verify with the authors first.
+
+Users must also abide by the
+[TCIA Data Usage Policy](https://www.cancerimagingarchive.net/data-usage-policies-and-restrictions/).
+Please cite the paper and the data DOI above.
+
+## Reproducing this mirror
+
+Level 2 of each remote `.svs` is read via HTTP byte-range requests — a `.svs`
+is a pyramidal TIFF, so pulling only that level costs **0.79% of each file
+(4.1 GB total instead of 522 GB)** and needs no Aspera client and no login.
+`CATCH.json` is then rasterized in file order at the same level.