Add dataset card

README.md

@@ -1,40 +1,51 @@
 ---
-dataset_info:
-  features:
-  - name: patient
-    dtype: string
-  - name: tissue
-    dtype:
-      class_label:
-        names:
-          '0': Unknown
-          '1': Breast
-          '2': Kidney
-          '3': Liver
-          '4': Prostate
-          '5': Bladder
-          '6': Colon
-          '7': Stomach
-  - name: image
-    dtype: image
-  - name: mask
-    dtype: image
-  - name: num_nuclei
-    dtype: int32
-  splits:
-  - name: train
-    num_bytes: 67579899
-    num_examples: 37
-  - name: test
-    num_bytes: 24579545
-    num_examples: 14
-  download_size: 92166762
-  dataset_size: 92159444
-configs:
-- config_name: default
-  data_files:
-  - split: train
-    path: data/train-*
-  - split: test
-    path: data/test-*
+license: cc-by-nc-sa-4.0
+task_categories:
+  - image-segmentation
+tags:
+  - medical
+  - histopathology
+  - nuclei
+  - h-and-e
+  - monuseg
+pretty_name: MoNuSeg
 ---
+
+# MoNuSeg (Multi-Organ Nucleus Segmentation)
+
+H&E-stained histopathology images (from TCGA WSIs at 40x magnification)
+with per-nucleus binary segmentation masks. MICCAI 2018 challenge.
+
+## Overview
+- **Modality:** H&E histopathology (brightfield microscopy)
+- **Image size:** 1000x1000 RGB
+- **Samples:** 37 train + 14 test = 51
+- **Organs (8 classes in `tissue`):** 0 Unknown, 1 Breast, 2 Kidney, 3 Liver,
+  4 Prostate, 5 Bladder, 6 Colon, 7 Stomach. Test also includes lung and brain
+  (labelled as tissue=0 Unknown here where not in the 8-class list).
+- **Ground truth:** single-annotator semantic binary mask (0 = tissue,
+  1 = nucleus), derived by OR-combining all per-nucleus instance polygons.
+
+## Columns
+| Column | Type | Notes |
+|---|---|---|
+| `patient` | string | TCGA patient ID (e.g. `TCGA-38-6178-01Z-00-DX1`) |
+| `tissue` | ClassLabel(8) | Organ label |
+| `image` | Image (RGB) | 1000x1000 H&E tile |
+| `mask` | Image (mode `1`) | 1000x1000 binary nuclei mask |
+| `num_nuclei` | int32 | Instance count used to build the mask |
+
+## Derivation
+Source: RationAI/MoNuSeg parquet mirror of the Grand Challenge 2018 data.
+The `instances` column of the source (a list of per-nucleus binary PIL
+masks) was merged by logical OR to produce a semantic `mask` column. No
+other preprocessing.
+
+## License
+CC BY-NC-SA 4.0. Underlying WSIs come from TCGA (public NIH data).
+
+## Citations
+- Kumar et al., "A Dataset and a Technique for Generalized Nuclear
+  Segmentation for Computational Pathology," IEEE TMI 36(7):1550-1560, 2017.
+- Kumar et al., "A Multi-organ Nucleus Segmentation Challenge," IEEE TMI,
+  2019.