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README.md

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+---
+license: other
+task_categories:
+  - image-segmentation
+  - image-classification
+language:
+  - en
+pretty_name: KidNet Renal Injury Pathology Dataset
+size_categories:
+  - 100<n<1K
+tags:
+  - pathology
+  - histopathology
+  - renal-injury
+  - kidney
+  - semantic-segmentation
+  - weakly-supervised-learning
+  - labelme
+---
+
+# KidNet Renal Injury Pathology Dataset
+
+KidNet is a curated hematoxylin and eosin (H&E) kidney pathology image dataset for renal injury recognition. Each sample contains one microscopy image and one LabelMe annotation file with polygon-level labels for renal tubules and related pathological structures.
+
+The dataset was built for small-sample renal injury modeling, especially:
+
+- pixel-level segmentation of `injury_tubules`
+- tile-level injury classification
+- weakly supervised heatmap localization
+- family-level generalization analysis across whole-slide-image (WSI) groups
+
+## Dataset Summary
+
+| Item | Count |
+| --- | ---: |
+| Images | 411 |
+| Annotation files | 411 |
+| WSI families | 11 |
+| Injury-positive images | 319 |
+| Injury-negative images | 92 |
+| Total annotated shapes | 16,932 |
+| `injury_tubules` annotations | 8,996 |
+
+## Directory Structure
+
+Each sample is stored in its own folder:
+
+```text
+KidNet/
+  WSI1_1/
+    WSI1_1.jpg
+    WSI1_1.json
+  WSI1_2/
+    WSI1_2.jpg
+    WSI1_2.json
+  ...
+```
+
+Each `.json` file follows the LabelMe format and contains:
+
+- `imagePath`: image filename
+- `imageHeight`, `imageWidth`: original image size
+- `shapes`: polygon or circle annotations
+- `label`: annotation class name
+- `points`: vertex coordinates in image pixel space
+- `shape_type`: usually `polygon`, with occasional `circle`
+
+Some LabelMe files may contain an `imageData` field. The paired `.jpg` file is the authoritative image file; `imageData` can be removed before upload if a smaller repository size is required.
+
+## Label Schema
+
+| Label | Count | Description |
+| --- | ---: | --- |
+| `injury_tubules` | 8,996 | Tubules annotated as injured; main binary segmentation target |
+| `healthy_tubules` | 3,756 | Tubules annotated as morphologically healthy |
+| `necrotic_tubules` | 2,369 | Necrotic tubule regions |
+| `cast` | 688 | Tubular cast regions |
+| `glomerulus` | 665 | Glomerular structures |
+| `unknown` | 458 | Ambiguous or uncertain regions |
+
+For binary renal injury segmentation, use `injury_tubules` as the positive class and all other pixels as background. For broader pathology modeling, the remaining labels can be used as auxiliary or multilabel targets.
+
+## Family-Level Distribution
+
+The recommended split unit is the WSI family, which is the prefix of each sample ID before the final numeric index. Random image-level splitting is not recommended because images from the same family may share staining, acquisition, tissue-source, and morphology patterns.
+
+| Family | Images | Injury-positive | Injury-negative | Injury annotations | Total shapes |
+| --- | ---: | ---: | ---: | ---: | ---: |
+| WSI1 | 20 | 2 | 18 | 5 | 669 |
+| WSI14 | 10 | 0 | 10 | 0 | 380 |
+| WSI15 | 10 | 0 | 10 | 0 | 383 |
+| WSI19 | 103 | 103 | 0 | 2,046 | 2,766 |
+| WSI2 | 20 | 5 | 15 | 18 | 720 |
+| WSI20 | 36 | 36 | 0 | 1,242 | 1,989 |
+| WSI3 | 34 | 20 | 14 | 770 | 1,658 |
+| WSI4 | 20 | 19 | 1 | 168 | 946 |
+| WSI5 | 118 | 115 | 3 | 4,587 | 5,780 |
+| WSI6 | 20 | 8 | 12 | 61 | 907 |
+| WSI7 | 20 | 11 | 9 | 99 | 734 |
+
+## Recommended Evaluation Protocol
+
+Use family-level held-out evaluation:
+
+1. Select one WSI family as the test family.
+2. Select another WSI family as validation.
+3. Train on the remaining families.
+4. Repeat across all 11 held-out families.
+
+This protocol is stricter than random image splitting and better measures generalization to unseen WSI families.
+
+Recommended metrics:
+
+- Segmentation: Dice, IoU, precision, recall, specificity
+- Tile classification: recall, precision, F1, balanced accuracy, AUROC when applicable
+- Heatmap localization: image-level recall, false-positive area, thresholded heatmap quality
+
+## Loading Example
+
+```python
+from pathlib import Path
+import json
+from PIL import Image
+
+root = Path("KidNet")
+samples = []
+
+for sample_dir in sorted(p for p in root.iterdir() if p.is_dir()):
+    image_path = next(sample_dir.glob("*.jpg"))
+    json_path = next(sample_dir.glob("*.json"))
+    with json_path.open("r", encoding="utf-8") as f:
+        ann = json.load(f)
+
+    labels = [shape["label"] for shape in ann.get("shapes", [])]
+    samples.append(
+        {
+            "sample_id": image_path.stem,
+            "family": image_path.stem.split("_")[0],
+            "image": Image.open(image_path).convert("RGB"),
+            "annotation": ann,
+            "has_injury": "injury_tubules" in labels,
+        }
+    )
+
+print(len(samples))
+```
+
+## Converting `injury_tubules` to a Binary Mask
+
+```python
+from PIL import Image, ImageDraw
+
+def injury_mask(annotation):
+    width = int(annotation["imageWidth"])
+    height = int(annotation["imageHeight"])
+    mask = Image.new("L", (width, height), 0)
+    draw = ImageDraw.Draw(mask)
+
+    for shape in annotation.get("shapes", []):
+        if shape.get("label") != "injury_tubules":
+            continue
+        points = [tuple(p) for p in shape.get("points", [])]
+        if shape.get("shape_type") == "polygon" and len(points) >= 3:
+            draw.polygon(points, fill=1)
+        elif shape.get("shape_type") == "circle" and len(points) >= 2:
+            (cx, cy), (px, py) = points[:2]
+            r = ((px - cx) ** 2 + (py - cy) ** 2) ** 0.5
+            draw.ellipse((cx - r, cy - r, cx + r, cy + r), fill=1)
+
+    return mask
+```
+
+## Intended Use
+
+This dataset is intended for academic research on renal injury recognition from pathology images. Suitable use cases include segmentation baselines, weakly supervised classification, heatmap localization, and small-sample generalization studies.
+
+The dataset is not intended for clinical diagnosis, treatment decisions, or deployment as a medical device.
+
+## Limitations
+
+- The dataset is small and strongly imbalanced across WSI families.
+- Some families are injury-rich, while others are sparse-positive or fully negative.
+- Labels are research annotations and should not be treated as exhaustive clinical ground truth.
+- Pixel-level boundaries can be uncertain for subtle tubular injury patterns.
+- Models evaluated with random image-level splits may report overly optimistic performance.
+
+## Ethics And Privacy
+
+The current release contains experimental kidney histopathology images and does not include human-identifiable personal information. Users should still follow institutional, animal research, and data-use requirements applicable to their own setting.
+
+## License
+
+License information should be confirmed by the dataset owner before public redistribution. The current metadata uses `other` as a placeholder. If the dataset is released publicly, replace it with the final approved license.
+
+## Citation
+
+If you use this dataset, please cite the project or competition report associated with KidNet. A formal citation can be added here after release.
+
+```bibtex
+@dataset{kidnet_renal_injury_pathology,
+  title = {KidNet Renal Injury Pathology Dataset},
+  year = {2026},
+  note = {H&E kidney pathology images with LabelMe annotations for renal injury recognition}
+}
+```