README.md

metadata

language:
  - en
license: unknown
task_categories:
  - object-detection
tags:
  - medical
  - cystoscopy
  - bladder-cancer
  - tumor-detection
  - video
size_categories:
  - 100M<n<1B
configs:
  - config_name: default
    data_files:
      - split: train
        path: data/train-*.parquet

Cystoscopy Tumor Detection

Cystoscopic video dataset with per-frame tumor bounding-box annotations, paired with patient-level clinical metadata. Flattened into a single train split with one row per video; videos are embedded in the Parquet shards as HF Video features and bounding boxes are stored inline.

Why no splits

This dataset ships one train split only. Use the patient_id column to build your own patient-grouped splits to prevent leakage. 30 unique patients.

Columns

Column	Type	Description
video	Video	Embedded video bytes (decode=False — returns {bytes, path}; cast to Video(decode=True) to decode frames, requires torchcodec).
video_id	string	Filename stem, e.g. P000_cystoscopy_track_000.
patient_id	int64	Patient id — use this for group-aware splitting.
boxes	struct	Per-box annotations as parallel lists (see below). n = len(boxes["frame"]) boxes for this video.
n_boxes	int64	Number of boxes for this video.
histological_type	string	Patient-level histology (e.g. Urothelial carcinoma pTaLG).
num_frames	int64	Patient-level total annotated frames.
light_mode	string	Imaging light mode (e.g. CLARA + CHROMA).

boxes struct fields

Each field is a list of length n_boxes; index i across all fields describes one box.

Field	Type	Description
track_id	int64	Annotation track within the video.
frame	int64	Frame number the box belongs to.
label	string	Box label (always tumor).
xtl, ytl, xbr, ybr	float32	Absolute pixel coordinates (CVAT format).
occluded	int64	Occlusion flag.
outside	int64	Outside flag.
keyframe	int64	Keyframe flag.
z_order	int64	Z-order.

Contents

• 173 cystoscopy .mp4 videos (~4 GB, embedded).
• 69 108 bounding boxes across 444 annotation tracks.
• 30 patients with clinical metadata.

Loading

from datasets import load_dataset
ds = load_dataset("milkyroad/B", split="train")
# video is not decoded by default (no torchcodec required to load)
print(ds[0]["video"])          # {'bytes': ..., 'path': 'P000_cystoscopy_track_000.mp4'}
print(ds[0]["n_boxes"])        # 233
boxes = ds[0]["boxes"]
print(boxes["frame"][0], boxes["label"][0], boxes["xtl"][0])

Decode video frames

To decode frames, install torchcodec and cast the column:

from datasets import Video
ds = ds.cast_column("video", Video(decode=True))

Group-aware split example

import random
ds = load_dataset("milkyroad/B", split="train")
pids = sorted({r["patient_id"] for r in ds})
random.Random(42).shuffle(pids)
n_test, n_val = 3, 3
test_pids = set(pids[:n_test])
val_pids = set(pids[n_test:n_test + n_val])
train = ds.filter(lambda r: r["patient_id"] not in test_pids and r["patient_id"] not in val_pids)
val   = ds.filter(lambda r: r["patient_id"] in val_pids)
test  = ds.filter(lambda r: r["patient_id"] in test_pids)

Notes

• Box coordinates are absolute pixel coordinates in the source video frames (CVAT format).
• Splits should be by patient to prevent leakage; patient_id is provided for this purpose.