| --- |
| language: |
| - en |
| license: cc-by-4.0 |
| task_categories: |
| - image-classification |
| tags: |
| - medical |
| - cystoscopy |
| - bladder-cancer |
| size_categories: |
| - 1K<n<10K |
| configs: |
| - config_name: default |
| data_files: |
| - split: train |
| path: data/train-*.parquet |
| --- |
| |
| # CystoDS |
|
|
| Cystoscopic image dataset with lesion annotations, segmentation masks (768 of |
| 8 067 images), and rich clinical metadata (class, subclass, stage, morphology, |
| modality). Flattened into a single `train` split with one row per image; |
| images are embedded in the Parquet shards as HF `Image` features and |
| segmentation polygons are stored inline. |
|
|
| ## Why no splits |
|
|
| This dataset ships **one `train` split only**. Use the `pid` (patient id) |
| column to build your own patient-grouped splits to prevent leakage. 160 unique |
| patients. |
|
|
| ## Columns |
|
|
| | Column | Type | Description | |
| |---|---|---| |
| | `image` | `Image` | Decoded PIL image (embedded bytes). | |
| | `filename` | `string` | 8-char de-identified PNG filename. | |
| | `pid` | `int64` | Patient id — use this for group-aware splitting. | |
| | `visit` | `float64` | Visit number (often NaN). | |
| | `lesion` | `string` | Lesion descriptor (often NaN). | |
| | `multifocal` | `string` | Multifocal flag (often NaN). | |
| | `bca` | `string` | Bladder cancer association (`Yes`/`No`). | |
| | `class` | `string` | Top-level class (see distribution below). | |
| | `subclass` | `string` | Subclass label. | |
| | `subclass2` | `string` | Secondary subclass. | |
| | `stage` | `string` | Tumor stage. | |
| | `morphology` | `string` | Morphology descriptor. | |
| | `modality` | `string` | Imaging modality (`WLC`, etc.). | |
| | `has_segmentation` | `int64` | `1` if a segmentation polygon exists, else `0`. | |
| | `segmentation` | `struct` | `null` when `has_segmentation == 0`, otherwise `{label: list[string], points: list[list[list[float]]]}` — each `points[i]` is a polygon `[x, y]` list matching `label[i]`. | |
|
|
| ## Class distribution |
|
|
| | Class | Count | |
| |---|---| |
| | Normal mucosa | 6386 | |
| | Malignant | 998 | |
| | Foreign bodies | 251 | |
| | Non-malignant | 221 | |
| | Anatomical landmarks | 211 | |
|
|
| ## Segmentation labels |
|
|
| 768 images have polygons (807 polygon shapes total). Labels include: `Tumor` |
| (459), `Air bubble` (216), `Right ureteral orifice` (50), `Left ureteral |
| orifice` (49), `Resection scar` (30), `Flat Tumor` (3). |
|
|
| ## Loading |
|
|
| ```python |
| from datasets import load_dataset |
| ds = load_dataset("milkyroad/C", split="train") |
| print(ds[0]["image"]) # PIL.Image |
| print(ds[0]["class"]) |
| # segmentation |
| if ds[0]["has_segmentation"]: |
| seg = ds[0]["segmentation"] |
| for label, poly in zip(seg["label"], seg["points"]): |
| print(label, len(poly), "points") |
| ``` |
|
|
| ## Group-aware split example |
|
|
| ```python |
| import random |
| ds = load_dataset("milkyroad/C", split="train") |
| pids = sorted({r["pid"] for r in ds if r["pid"] is not None}) |
| random.Random(42).shuffle(pids) |
| n_test, n_val = 16, 16 |
| test_pids = set(pids[:n_test]) |
| val_pids = set(pids[n_test:n_test + n_val]) |
| train = ds.filter(lambda r: r["pid"] not in test_pids and r["pid"] not in val_pids) |
| val = ds.filter(lambda r: r["pid"] in val_pids) |
| test = ds.filter(lambda r: r["pid"] in test_pids) |
| ``` |
|
|
| ## Notes |
|
|
| - 160 unique patients (`pid`). Build splits by **patient** to prevent leakage. |
| - `segmentation` is `null` for images without a mask (`has_segmentation == 0`). |
| - Original source: https://osf.io/xvdhy/ |
|
|
