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README.md

@@ -1,39 +1,90 @@
 ---
-dataset_info:
-  features:
-  - name: image
-    dtype: image
-  - name: inst_map
-    dtype: image
-  - name: type_map
-    dtype: image
-  - name: tissue
-    dtype: int32
-  - name: tissue_name
-    dtype: string
-  - name: fold
-    dtype: int32
-  - name: sample_id
-    dtype: string
-  splits:
-  - name: fold1
-    num_bytes: 306763992
-    num_examples: 2656
-  - name: fold2
-    num_bytes: 288997343
-    num_examples: 2523
-  - name: fold3
-    num_bytes: 316302533
-    num_examples: 2722
-  download_size: 911961186
-  dataset_size: 912063868
-configs:
-- config_name: default
-  data_files:
-  - split: fold1
-    path: data/fold1-*
-  - split: fold2
-    path: data/fold2-*
-  - split: fold3
-    path: data/fold3-*
+license: cc-by-nc-sa-4.0
+task_categories:
+  - image-segmentation
+tags:
+  - medical
+  - histopathology
+  - h-and-e
+  - nuclei
+  - instance-segmentation
+  - pan-cancer
+  - tcga
+  - gtex
+size_categories:
+  - 1K<n<10K
 ---
+
+# PanNuke
+
+Pan-Cancer H&E Nuclei Instance Segmentation and Classification dataset
+(Gamper et al., ECDP 2019; arXiv:2003.10778). Mirrored from the
+[Warwick TIA Centre release](https://warwick.ac.uk/fac/cross_fac/tia/data/pannuke).
+
+## Composition
+
+- **7,901** RGB patches of 256x256 at 40x magnification (~0.25 um/pixel)
+- **19** tissue types pooled from TCGA / GTEx (Breast, Colon, Lung, Kidney,
+  Prostate, Stomach, Ovarian, Bladder, Esophagus, Pancreatic, Thyroid, Skin,
+  Cervix, Adrenal_gland, Bile-duct, Liver, HeadNeck, Testis, Uterus)
+- **~189,744** annotated nuclei, multi-pathologist QC
+- **3 folds** (PanNuke is designed for 3-fold cross-validation):
+  - `fold1`: 2,656 patches
+  - `fold2`: 2,523 patches
+  - `fold3`: 2,722 patches
+
+## Schema
+
+| Field         | Type                          | Description                                           |
+|---------------|-------------------------------|-------------------------------------------------------|
+| `image`       | PIL RGB 256x256               | H&E patch, uint8                                      |
+| `inst_map`    | PIL grayscale uint16 256x256  | Global per-pixel instance ID (0 = background)         |
+| `type_map`    | PIL grayscale uint8 256x256   | Semantic class per pixel (see table below)            |
+| `tissue`      | int                           | Tissue ID 0-18 (alphabetical)                         |
+| `tissue_name` | str                           | Tissue type name                                      |
+| `fold`        | int                           | Source fold (1, 2, or 3)                              |
+| `sample_id`   | str                           | Unique ID, `foldN_NNNN`                               |
+
+### Semantic class encoding (`type_map`)
+
+| Value | Class                    |
+|-------|--------------------------|
+| 0     | Background               |
+| 1     | Neoplastic               |
+| 2     | Inflammatory             |
+| 3     | Connective / Soft tissue |
+| 4     | Dead                     |
+| 5     | Epithelial               |
+
+### Instance map (`inst_map`)
+
+Each connected nucleus is assigned a unique 16-bit integer ID per patch
+(starting from 1). IDs are derived from the original 6-channel mask
+arrays released by Warwick: for each foreground class channel, instance
+IDs are offset by the running count of nuclei in previous channels so
+the result is globally unique within the patch.
+
+## License
+
+CC BY-NC-SA 4.0 (research / non-commercial use). Same license as the
+original Warwick release.
+
+## Citation
+
+```bibtex
+@article{gamper2020pannuke,
+  title={PanNuke Dataset Extension, Insights and Baselines},
+  author={Gamper, Jevgenij and Koohbanani, Navid Alemi and Graham, Simon and Jahanifar, Mostafa and Benet, Ksenija and Khurram, Syed Ali and Azam, Ayesha and Hewitt, Katherine and Rajpoot, Nasir},
+  journal={arXiv preprint arXiv:2003.10778},
+  year={2020}
+}
+
+@inproceedings{gamper2019pannuke,
+  title={Pannuke: An open pan-cancer histology dataset for nuclei instance segmentation and classification},
+  author={Gamper, Jevgenij and Koohbanani, Navid Alemi and Benet, Ksenija and Khuram, Ali and Rajpoot, Nasir},
+  booktitle={European Congress on Digital Pathology},
+  pages={11--19},
+  year={2019},
+  organization={Springer}
+}
+```