Add dataset card

README.md

@@ -1,146 +1,169 @@
 ---
-dataset_info:
-- config_name: he
-  features:
-  - name: image
-    dtype: image
-  - name: mask
-    dtype: image
-  - name: image_with_context
-    dtype: image
-  - name: mask_with_context
-    dtype: image
-  - name: validation_fold
-    dtype: string
-  - name: patient_id
-    dtype: int32
-  - name: roi_id
-    dtype: int32
-  - name: name
-    dtype: string
-  - name: source
-    dtype: string
-  - name: specimen_type
-    dtype: string
-  - name: organ
-    dtype: string
-  - name: histological_subtype
-    dtype: string
-  - name: stain
-    dtype: string
-  - name: scanner
-    dtype: string
-  - name: shape
-    dtype: string
-  - name: area_mm2
-    dtype: float32
-  - name: original_tcga_id
-    dtype: string
-  splits:
-  - name: train
-    num_bytes: 3963751573
-    num_examples: 408
-  download_size: 3964118620
-  dataset_size: 3963751573
-- config_name: nuclei
-  features:
-  - name: image
-    dtype: image
-  - name: patient_id
-    dtype: int32
-  - name: roi_id
-    dtype: int32
-  - name: name
-    dtype: string
-  - name: source
-    dtype: string
-  - name: specimen_type
-    dtype: string
-  - name: organ
-    dtype: string
-  - name: histological_subtype
-    dtype: string
-  - name: stain
-    dtype: string
-  - name: scanner
-    dtype: string
-  - name: shape
-    dtype: string
-  - name: area_mm2
-    dtype: float32
-  - name: original_tcga_id
-    dtype: string
-  splits:
-  - name: train
-    num_bytes: 96942013
-    num_examples: 67
-  - name: validation
-    num_bytes: 55692546
-    num_examples: 36
-  - name: test
-    num_bytes: 65050351
-    num_examples: 32
-  download_size: 217694424
-  dataset_size: 217684910
-- config_name: pdl1
-  features:
-  - name: image
-    dtype: image
-  - name: patient_id
-    dtype: int32
-  - name: roi_id
-    dtype: int32
-  - name: name
-    dtype: string
-  - name: source
-    dtype: string
-  - name: specimen_type
-    dtype: string
-  - name: organ
-    dtype: string
-  - name: histological_subtype
-    dtype: string
-  - name: stain
-    dtype: string
-  - name: scanner
-    dtype: string
-  - name: shape
-    dtype: string
-  - name: area_mm2
-    dtype: float32
-  - name: original_tcga_id
-    dtype: string
-  splits:
-  - name: train
-    num_bytes: 1057978657
-    num_examples: 185
-  - name: validation
-    num_bytes: 483032882
-    num_examples: 61
-  - name: test
-    num_bytes: 51005544
-    num_examples: 98
-  download_size: 1592071431
-  dataset_size: 1592017083
+license: cc-by-nc-sa-4.0
+task_categories:
+  - image-segmentation
+  - object-detection
+language:
+  - en
+tags:
+  - medical
+  - pathology
+  - histopathology
+  - h-and-e
+  - pd-l1
+  - lung
+  - nsclc
+  - ignite
+size_categories:
+  - n<1K
 configs:
-- config_name: he
-  data_files:
-  - split: train
-    path: he/train-*
-- config_name: nuclei
-  data_files:
-  - split: train
-    path: nuclei/train-*
-  - split: validation
-    path: nuclei/validation-*
-  - split: test
-    path: nuclei/test-*
-- config_name: pdl1
-  data_files:
-  - split: train
-    path: pdl1/train-*
-  - split: validation
-    path: pdl1/validation-*
-  - split: test
-    path: pdl1/test-*
+  - config_name: he
+    data_files:
+      - split: train
+        path: he/train-*
+  - config_name: pdl1
+    data_files:
+      - split: train
+        path: pdl1/train-*
+      - split: validation
+        path: pdl1/validation-*
+      - split: test
+        path: pdl1/test-*
+  - config_name: nuclei
+    data_files:
+      - split: train
+        path: nuclei/train-*
+      - split: validation
+        path: nuclei/validation-*
+      - split: test
+        path: nuclei/test-*
 ---
+
+# IGNITE Data Toolkit (mirror)
+
+Mirror of the **IGNITE Data Toolkit** by Spronck et al. (Radboud UMC),
+originally distributed on [Zenodo (10.5281/zenodo.15674785)](https://zenodo.org/records/15674785)
+and accompanied by [DIAGNijmegen/ignite-data-toolkit](https://github.com/DIAGNijmegen/ignite-data-toolkit).
+The dataset accompanies *"A tissue and cell-level annotated H&E and PD-L1 histopathology image
+dataset in non-small cell lung cancer"* ([arXiv:2507.16855](https://arxiv.org/abs/2507.16855)).
+
+**License:** [CC BY-NC-SA 4.0](https://creativecommons.org/licenses/by-nc-sa/4.0/) —
+non-commercial, share-alike. Attribution to the original authors is required.
+
+## Contents
+
+155 unique patients, 887 fully annotated regions of interest from a multi-stain,
+multi-centric, multi-scanner cohort (Radboud UMC, Sacro Cuore Don Calabria, TCGA-LUAD/LUSC).
+The release splits into three task-defined subsets, exposed here as named configs:
+
+| Config   | Task                                  | ROIs |
+|----------|---------------------------------------|-----:|
+| `he`     | H&E tissue compartment segmentation   |  408 |
+| `pdl1`   | PD-L1+ tumor cell detection           |  344 |
+| `nuclei` | PD-L1 IHC nuclei detection            |  135 |
+
+## H&E tissue segmentation (`he` config)
+
+Each row is **one ROI** with paired image/mask in two field-of-view variants:
+
+| Column                 | Type    | Description                                                  |
+|------------------------|---------|--------------------------------------------------------------|
+| `image`                | `Image` | Base ROI (inner annotated region only)                       |
+| `mask`                 | `Image` | 16-class pixel mask aligned to `image`                       |
+| `image_with_context`   | `Image` | Same ROI extended to a 1792x1792 view (annotated context)    |
+| `mask_with_context`    | `Image` | 16-class pixel mask aligned to `image_with_context`          |
+| `validation_fold`      | string  | 5-fold CV assignment (`fold0`..`fold4`)                      |
+| `patient_id`           | int32   | Patient identifier                                           |
+| `roi_id`               | int32   | ROI index within patient                                     |
+| `name`                 | string  | `patient<id>_he_roi<idx>` (matches the original release)     |
+| `source`               | string  | `rumc`, `scdc`, or `tcga`                                    |
+| `specimen_type`        | string  | `resection`, `biopsy`, or `tissue_microarray`                |
+| `organ`                | string  | Anatomical site (lung, liver, bone, brain, ...)              |
+| `histological_subtype` | string  | `adenocarcinoma`, `squamous_cell_carcinoma`, ...             |
+| `stain`                | string  | Always `H&E` for this config                                 |
+| `scanner`              | string  | WSI scanner model                                            |
+| `shape`                | string  | Original `(height, width)` tuple as a string                 |
+| `area_mm2`             | float32 | Annotated tissue area in mm^2                                |
+| `original_tcga_id`     | string  | TCGA case ID for TCGA-sourced ROIs (empty otherwise)         |
+
+Labels (also shipped as `he_label_map.json`):
+
+| ID | Class                | ID | Class                |
+|---:|----------------------|---:|----------------------|
+| 0  | Unannotated          | 9  | Erythrocytes         |
+| 1  | Background           | 10 | Bronchial epithelium |
+| 2  | Tumor epithelium     | 11 | Mucus/Plasma/Fluids  |
+| 3  | Reactive epithelium  | 12 | Cartilage/Bone       |
+| 4  | Stroma               | 13 | Macrophages          |
+| 5  | Inflammation         | 14 | Muscle               |
+| 6  | Alveolar tissue      | 15 | Liver                |
+| 7  | Fatty tissue         | 16 | Keratinization       |
+| 8  | Necrotic tissue      |    |                      |
+
+> **Mirror-specific note:** In the original Zenodo release, base ROI masks (the inner-crop view)
+> store class label `L` as the byte value `(256 - L) mod 256` (e.g. label 4 -> byte 252).
+> The `_with_context` masks already store labels directly. In this HuggingFace mirror **both
+> `mask` and `mask_with_context` are written with the canonical 0..16 labels** — base masks were
+> pre-decoded during upload, so downstream code does not need to handle the encoding quirk.
+
+The `he` config has a single `train` split — use `validation_fold` to assemble
+5-fold cross-validation as recommended by the paper.
+
+## PD-L1 / nuclei detection (`pdl1`, `nuclei` configs)
+
+These configs hold images plus per-image metadata only (same columns as `he` except no
+`mask`/`_with_context` fields and no `validation_fold`). The detection ground truth is
+in **MS-COCO JSON** format and is shipped as raw sidecar files because COCO-style nested
+annotations are a poor fit for columnar parquet:
+
+| Path                                | Subset | Notes                                      |
+|-------------------------------------|--------|--------------------------------------------|
+| `coco/pdl1_annotations.json`        | pdl1   | Main annotations                           |
+| `coco/pdl1_test_set_all_readers.json` | pdl1 | Multi-reader test set                      |
+| `coco/nuclei_annotations.json`      | nuclei | Main annotations                           |
+| `coco/nuclei_test_set_all_readers.json` | nuclei | Multi-reader test set                  |
+
+Use the row's `name` field (== `image_id` in COCO `images[*].file_name = "<name>.png"`)
+to look up bounding-box / point annotations.
+
+Splits follow `data_overview.csv` directly (no fold column for the detection tasks).
+
+## Loading
+
+```python
+from datasets import load_dataset
+
+# H&E tissue segmentation (single train split, use validation_fold for 5-fold CV)
+he = load_dataset("Angelou0516/IGNITE", "he", split="train")
+print(he[0]["mask"])  # PIL Image L-mode, labels 0..16
+
+# PD-L1+ tumor cell detection
+pdl1 = load_dataset("Angelou0516/IGNITE", "pdl1")
+
+# PD-L1 IHC nuclei detection
+nuc = load_dataset("Angelou0516/IGNITE", "nuclei")
+```
+
+For detection COCO annotations, download the JSON sidecars with `huggingface_hub.hf_hub_download`.
+
+## Sidecar files (raw)
+
+- `he_label_map.json` — class id -> name
+- `data_overview.csv` — per-ROI metadata (887 rows x 17 cols), authoritative for splits / folds
+- `coco/*.json` — detection annotations (4 files, see table above)
+
+## Citation
+
+```bibtex
+@article{Spronck2025ignite,
+  title   = {A tissue and cell-level annotated H\&E and PD-L1 histopathology image dataset in non-small cell lung cancer},
+  author  = {Spronck, Joey and van Eekelen, Leander and van Midden, Dominique and others},
+  journal = {arXiv preprint arXiv:2507.16855},
+  year    = {2025},
+  doi     = {10.48550/arXiv.2507.16855}
+}
+```
+
+Mirror maintained by `Angelou0516`. For the official authoritative release see the
+[Zenodo record](https://zenodo.org/records/15674785) and
+[GitHub toolkit](https://github.com/DIAGNijmegen/ignite-data-toolkit).