README.md

metadata

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

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

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/