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README.md

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+---
+dataset_info:
+  features:
+  - name: image_id
+    dtype: string
+  - name: image
+    dtype: image
+  - name: filename
+    dtype: string
+  - name: width
+    dtype: int32
+  - name: height
+    dtype: int32
+  - name: split
+    dtype: string
+  - name: label_tumor
+    dtype: int8
+  - name: label_stroma
+    dtype: int8
+  - name: label_normal
+    dtype: int8
+  - name: mask
+    dtype: image
+  - name: background_mask
+    dtype: image
+  splits:
+  - name: train
+    num_examples: 10091
+  - name: validation
+    num_examples: 40
+  - name: test
+    num_examples: 80
+license: cc-by-4.0
+task_categories:
+- image-segmentation
+tags:
+- medical
+- pathology
+- lung-cancer
+---
+
+# WSSS4LUAD Dataset
+
+This is the WSSS4LUAD dataset for weakly-supervised tissue semantic segmentation in lung adenocarcinoma.
+
+## Dataset Description
+
+The dataset contains histopathology image patches from lung adenocarcinoma tissue samples, designed for weakly-supervised semantic segmentation tasks.
+
+## Dataset Structure
+
+- **Training**: 10,091 patches with image-level labels
+- **Validation**: 40 patches with pixel-level masks
+- **Test**: 80 patches with pixel-level masks
+
+## Classes
+
+The dataset includes 3 tissue classes:
+1. **Tumor epithelial tissue** (label_tumor)
+2. **Tumor-associated stroma tissue** (label_stroma)
+3. **Normal tissue** (label_normal)
+
+## Features
+
+- `image_id`: Unique identifier for each image
+- `image`: The histopathology image patch (PNG format, embedded)
+- `filename`: Original filename
+- `width`: Image width in pixels
+- `height`: Image height in pixels
+- `split`: Dataset split (train/validation/test)
+- `label_tumor`: Binary label for tumor epithelial tissue (training only)
+- `label_stroma`: Binary label for tumor stroma tissue (training only)
+- `label_normal`: Binary label for normal tissue (training only)
+- `mask`: Pixel-level segmentation mask (validation/test only)
+- `background_mask`: Background region mask (validation/test only)
+
+## Usage
+
+```python
+from datasets import load_dataset
+
+# Load the dataset
+dataset = load_dataset("USERNAME/WSSS4LUAD")
+
+# Access different splits
+train_data = dataset['train']
+val_data = dataset['validation']
+test_data = dataset['test']
+
+# Example: Load and display an image
+example = train_data[0]
+image = example['image']  # PIL Image
+labels = {
+    'tumor': example['label_tumor'],
+    'stroma': example['label_stroma'],
+    'normal': example['label_normal']
+}
+```
+
+## Citation
+
+If you use this dataset, please cite the original WSSS4LUAD paper.
+
+## License
+
+This dataset is released under the Creative Commons Attribution 4.0 International (CC BY 4.0) license.

dataset_info.json

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+{
+  "dataset_name": "WSSS4LUAD",
+  "description": "Weakly-Supervised Tissue Semantic Segmentation for Lung Adenocarcinoma",
+  "num_classes": 3,
+  "classes": [
+    "tumor_epithelial",
+    "tumor_stroma",
+    "normal_tissue"
+  ],
+  "total_samples": 10211,
+  "splits": {
+    "train": {
+      "num_examples": 10091,
+      "file_size_mb": 1171.9069919586182,
+      "max_row_group_mb": 1.5423202514648438,
+      "num_row_groups": 1010
+    },
+    "validation": {
+      "num_examples": 40,
+      "file_size_mb": 150.56257247924805,
+      "max_row_group_mb": 142.52832126617432,
+      "num_row_groups": 4
+    },
+    "test": {
+      "num_examples": 80,
+      "file_size_mb": 234.02422714233398,
+      "max_row_group_mb": 156.98893070220947,
+      "num_row_groups": 8
+    }
+  },
+  "image_format": "PNG (embedded as bytes)",
+  "license": "CC BY 4.0",
+  "batch_size_used": 10
+}

test.parquet

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train.parquet

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validation.parquet

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