Update README: add he test split path; document train/test sizes

README.md

@@ -1,90 +1,44 @@
 ---
 license: cc-by-nc-sa-4.0
 task_categories:
-- image-segmentation
-- object-detection
+  - image-segmentation
+  - object-detection
 language:
-- en
+  - en
 tags:
-- medical
-- pathology
-- histopathology
-- h-and-e
-- pd-l1
-- lung
-- nsclc
-- ignite
+  - medical
+  - pathology
+  - histopathology
+  - h-and-e
+  - pd-l1
+  - lung
+  - nsclc
+  - ignite
 size_categories:
-- n<1K
+  - n<1K
 configs:
-- config_name: he
-  data_files:
-  - split: train
-    path: he/train-*
-  - split: test
-    path: he/test-*
-- 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-*
-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: 2700615464
-    num_examples: 269
-  - name: test
-    num_bytes: 1263135677
-    num_examples: 139
-  download_size: 3964084999
-  dataset_size: 3963751141
+  - config_name: he
+    data_files:
+      - split: train
+        path: he/train-*
+      - split: test
+        path: he/test-*
+  - 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)
@@ -95,7 +49,7 @@ and accompanied by [DIAGNijmegen/ignite-data-toolkit](https://github.com/DIAGNij
 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/) —
+**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
@@ -112,6 +66,13 @@ The release splits into three task-defined subsets, exposed here as named config
 
 ## H&E tissue segmentation (`he` config)
 
+Splits follow `data_overview.csv`:
+
+| Split | ROIs | Notes                                                            |
+|-------|-----:|------------------------------------------------------------------|
+| train |  269 | Train pool — paper uses 5-fold CV via the `validation_fold` col. |
+| test  |  139 | Held-out evaluation set (62 TCGA + 77 Radboud ROIs).             |
+
 Each row is **one ROI** with paired image/mask in two field-of-view variants:
 
 | Column                 | Type    | Description                                                  |
@@ -120,7 +81,7 @@ Each row is **one ROI** with paired image/mask in two field-of-view variants:
 | `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`)                      |
+| `validation_fold`      | string  | 5-fold CV assignment (`fold0`..`fold4`); empty for test rows |
 | `patient_id`           | int32   | Patient identifier                                           |
 | `roi_id`               | int32   | ROI index within patient                                     |
 | `name`                 | string  | `patient<id>_he_roi<idx>` (matches the original release)     |
@@ -148,14 +109,18 @@ Labels (also shipped as `he_label_map.json`):
 | 7  | Fatty tissue         | 16 | Keratinization       |
 | 8  | Necrotic tissue      |    |                      |
 
+The paper's evaluation pipeline treats class `0` ("Unannotated", i.e. surrounding context
+in `_with_context` masks) as an **ignore label** during Dice/IoU computation. Downstream
+loaders should mirror that to reproduce paper-comparable scores.
+
 > **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
+> `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.
+The paper recommends training-time 5-fold CV via the `validation_fold` column on the `train`
+split, and reports final numbers on the held-out `test` split.
 
 ## PD-L1 / nuclei detection (`pdl1`, `nuclei` configs)
 
@@ -181,9 +146,10 @@ Splits follow `data_overview.csv` directly (no fold column for the detection tas
 ```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
+# H&E tissue segmentation
+he_train = load_dataset("Angelou0516/IGNITE", "he", split="train")  # 269 ROIs
+he_test  = load_dataset("Angelou0516/IGNITE", "he", split="test")   # 139 ROIs
+print(he_test[0]["mask_with_context"])  # PIL Image L-mode, labels 0..16
 
 # PD-L1+ tumor cell detection
 pdl1 = load_dataset("Angelou0516/IGNITE", "pdl1")