Upload README.md with huggingface_hub

README.md

@@ -0,0 +1,208 @@
+---
+license: cc-by-4.0
+task_categories:
+  - image-segmentation
+  - image-classification
+language:
+  - en
+tags:
+  - medical
+  - radiology
+  - chest-ct
+  - anatomy
+  - segmentation
+  - ct-scan
+  - projection
+pretty_name: RadGenome-ChestCT Views
+size_categories:
+  - 10K<n<100K
+configs:
+- config_name: default
+  data_files:
+  - split: train
+    path: data/train/*.parquet
+  - split: validation
+    path: data/validation/*.parquet
+dataset_info:
+  features:
+  - name: volume_id
+    dtype: string
+  - name: split
+    dtype: string
+  - name: image_pa
+    dtype: image
+  - name: image_ll
+    dtype: image
+  - name: mask_labels_pa
+    sequence: string
+  - name: masks_pa
+    sequence: image
+  - name: mask_labels_ll
+    sequence: string
+  - name: masks_ll
+    sequence: image
+---
+
+# RadGenome-ChestCT Views
+
+A Parquet dataset of chest CT 2D projection views with per-anatomy binary segmentation masks,
+derived from the [RadGenome-ChestCT](https://huggingface.co/datasets/RadGenome/RadGenome-ChestCT) dataset
+(originally based on [CT-RATE](https://huggingface.co/datasets/ibrahimhamamci/CT-RATE)).
+
+Each row represents one CT volume and contains its PA (posteroanterior) and LL (lateral)
+projection images, plus binary anatomy masks for all 210 anatomical structures.
+
+---
+
+## Dataset Summary
+
+| Property | Value |
+|---|---|
+| **Volumes** | 25,692 total |
+| **Views per volume** | 2 (PA + LL) |
+| **Anatomy classes** | 210 structures |
+| **License** | CC-BY-4.0 |
+| **Source** | RadGenome-ChestCT / CT-RATE |
+
+### Splits
+
+| Split | Volumes |
+|---|---|
+| train | 24,128 |
+| validation | 1,564 |
+
+---
+
+## Dataset Structure
+
+### Data Fields
+
+| Column | Type | Description |
+|---|---|---|
+| `volume_id` | `str` | Unique volume identifier, e.g. `train_1_a_1`. |
+| `split` | `str` | Dataset split: `train` or `validation`. |
+| `image_pa` | `Image` | PA (posteroanterior, front) chest projection image (JPEG). |
+| `image_ll` | `Image` | LL (lateral, side) chest projection image (JPEG). |
+| `mask_labels_pa` | `List[str]` | Anatomy structure names for the PA masks, e.g. `["lung", "heart", ...]`. Parallel to `masks_pa`. |
+| `masks_pa` | `List[Image]` | Binary anatomy segmentation masks (one per structure) for the PA view. White pixels = structure present. |
+| `mask_labels_ll` | `List[str]` | Anatomy structure names for the LL masks. Parallel to `masks_ll`. |
+| `masks_ll` | `List[Image]` | Binary anatomy segmentation masks for the LL view. |
+
+### Anatomy Label Universe
+
+The dataset contains masks for **210 anatomical structures**, covering:
+- **Lungs**: left/right lung, upper/lower/middle lobes, lung nodule, tumor, effusion
+- **Heart**: chambers (atria, ventricles), myocardium, ascending aorta, pulmonary artery/vein
+- **Vessels**: aorta, inferior/superior vena cava, carotid arteries, subclavian arteries, brachiocephalic vessels, iliac vessels, renal vessels
+- **Skeleton**: ribs (1–12, left/right), thoracic vertebrae (T1–T12), cervical/lumbar vertebrae, sternum, clavicles, scapulae, humerus, femur
+- **Abdominal organs**: liver (with segments), spleen, pancreas, kidneys, gallbladder, stomach, intestine, esophagus
+- **Endocrine/other**: thyroid, adrenal glands, thymus, trachea, larynx, spinal cord
+
+The full ordered list is in [`label_universe.json`](./label_universe.json) at the repo root.
+Use it to map labels to fixed class indices for consistent multi-label training.
+
+---
+
+## Usage
+
+### Load with 🤗 Datasets
+
+```python
+from datasets import load_dataset
+
+ds = load_dataset("EvidenceAIResearch/radgenome-anatomy")
+print(ds)
+# DatasetDict({
+#     train: Dataset({features: ['volume_id', 'split', 'image_pa', 'image_ll', ...], num_rows: 24,128})
+#     validation: Dataset({features: ['volume_id', 'split', 'image_pa', 'image_ll', ...], num_rows: 1,564})
+# })
+```
+
+### Access images
+
+```python
+from PIL import Image
+import io
+
+row = ds["train"][0]
+print(row["volume_id"])   # e.g. "train_1_a_1"
+
+# Decode PA image
+pa_img = Image.open(io.BytesIO(row["image_pa"]["bytes"]))
+ll_img = Image.open(io.BytesIO(row["image_ll"]["bytes"]))
+
+pa_img.show()
+```
+
+### Working with masks
+
+Each sample contains a list of binary masks with corresponding label names:
+
+```python
+from PIL import Image
+import io, numpy as np
+
+row = ds["train"][0]
+
+# Map label → mask for the LL view
+mask_map = {
+    label: Image.open(io.BytesIO(img["bytes"]))
+    for label, img in zip(row["mask_labels_ll"], row["masks_ll"])
+}
+
+# Example: get the lung mask as a numpy array
+lung_mask = np.array(mask_map["lung"])  # shape: (H, W), values 0 or 255
+
+# Build a multi-label tensor [num_classes, H, W] using the label universe
+import json, urllib.request
+# label_universe.json is in the repo root
+universe = json.loads(open("label_universe.json").read())
+label_to_idx = {l: i for i, l in enumerate(universe)}
+
+H, W = lung_mask.shape
+multi_label = np.zeros((len(universe), H, W), dtype=np.uint8)
+for label, img in zip(row["mask_labels_ll"], row["masks_ll"]):
+    if label in label_to_idx:
+        arr = np.array(Image.open(io.BytesIO(img["bytes"])))
+        multi_label[label_to_idx[label]] = (arr > 128).astype(np.uint8)
+```
+
+### Streaming (large dataset)
+
+```python
+ds = load_dataset("EvidenceAIResearch/radgenome-anatomy", streaming=True)
+for row in ds["train"]:
+    pa_img = Image.open(io.BytesIO(row["image_pa"]["bytes"]))
+    # process ...
+    break
+```
+
+---
+
+## Source & Citation
+
+This dataset is derived from **RadGenome-ChestCT**, which is built on **CT-RATE**.
+If you use this dataset, please cite both:
+
+```bibtex
+@article{radgenome2024,
+  title={RadGenome-Chest CT: A Grounded Vision-Language Dataset for Chest CT Analysis},
+  author={RadGenome Team},
+  year={2024},
+}
+
+@article{hamamci2024foundation,
+  title={A foundation model utilizing chest CT volumes and radiology reports for supervised-level zero-shot detection of abnormalities},
+  author={Hamamci, Ibrahim Ethem and others},
+  journal={arXiv preprint arXiv:2403.17834},
+  year={2024},
+}
+```
+
+---
+
+## License
+
+[CC-BY-4.0](https://creativecommons.org/licenses/by/4.0/) — derived from CT-RATE.
+Commercial use is not permitted without prior permission from the original data providers.
+See the [original dataset terms](https://huggingface.co/datasets/RadGenome/RadGenome-ChestCT) for full conditions.