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metadata 
license: unknown
tags:
  - medical-imaging
  - cystoscopy
  - bladder-cancer
  - cancer-detection
  - histological-grade
pretty_name: Unified Cystoscopy Cancer Detection (Dataset F)
size_categories:
  - 10K<n<100K
dataset_info:
  features:
    - name: image
      dtype: image
    - name: cancer_label
      dtype:
        class_label:
          names:
            '0': non_cancer
            '1': cancer
    - name: grade_label
      dtype:
        class_label:
          names:
            '0': low_grade
            '1': high_grade
            '2': not_applicable
    - name: subclass_label
      dtype:
        class_label:
          names:
            '0': malignant
            '1': non_malignant
            '2': normal
            '3': landmark
            '4': foreign_body
    - name: source_dataset
      dtype: string
    - name: original_filename
      dtype: string
    - name: patient_id
      dtype: int32
    - name: imaging_type
      dtype:
        class_label:
          names:
            '0': WLI
            '1': NBI
            '2': BLC
    - name: target3
      dtype:
        class_label:
          names:
            '0': malignant
            '1': non_malignant
            '2': non_roi
    - name: track_id
      dtype: string
    - name: cv_fold
      dtype: int64
  splits:
    - name: train
      num_bytes: 5493071831
      num_examples: 10067
    - name: validation
      num_bytes: 853196271
      num_examples: 2157
    - name: test
      num_bytes: 979011150
      num_examples: 2152
  download_size: 7325138684
  dataset_size: 7325279252
configs:
  - config_name: default
    data_files:
      - split: train
        path: data/train-*
      - split: validation
        path: data/validation-*
      - split: test
        path: data/test-*

Unified Cystoscopy Cancer Detection (Dataset F)

Dataset Description

Dataset F is derived from Dataset E by removing every PUNLMP frame and re-deriving a patient-level train / validation / test split from scratch. It is prepared for 3-class deep learning (malignant vs non-malignant vs non-ROI) and keeps the same per-image schema as Dataset E (image, cancer_label, grade_label, subclass_label, source_dataset, original_filename, patient_id, imaging_type, target3, track_id, cv_fold).

  • Total images: 14,376 (E had 14,921; 545 PUNLMP frames removed)
  • Unique patients / cases: 209 (E had 212; the 3 PUNLMP-only source-B patients 6, 8, 29 are dropped)
  • Modalities: White-Light Imaging (WLI), Narrow-Band Imaging (NBI), Blue-Light Cystoscopy (BLC)
  • Splits: train / validation / test (70 / 15 / 15), patient-level with no leakage
  • 5-fold cross-validation on the train pool (cv_fold 0-4; -1 for val/test)

Relationship to Dataset E

Dataset E Dataset F
Total images 14,921 14,376
Patients 212 209
non_malignant images 900 355
PUNLMP frames 545 (source-B non_malignant) 0 (excluded)
Split derivation seed 16, 14 swaps seed 17, 12 swaps (re-searched)

PUNLMP Exclusion

In Dataset E, PUNLMP (Papillary Urothelial Neoplasm of Low Malignant Potential) is the histological_type of 12 source-B videos (milkyroad/B), and every source-B non_malignant frame in E comes from those PUNLMP videos (E patients 6, 8, 29; tracks 028, 029, 032-037, 162-165). Therefore:

PUNLMP frames  ==  (source_dataset == 'B') AND (target3 == 'non_malignant')

Removing them drops the non_malignant class from 900 -> 355 images, all now coming from sources C and D. Because this makes a good patient-level split much harder, the split is re-derived from scratch (Dataset F does not inherit Dataset E's split assignment).

Source Datasets

Code Source Type Images in F Patients in F
B Cystoscopy video dataset (stride-8 frames) Video frames 4,555 27
C CystoDS Still cystoscopy images 8,067 160
D Kaggle cystoscopy frames Still cystoscopy frames 1,754 22

Source-B majority-class frames (malignant + non-ROI) in E are already capped at 60 per (patient, track); Dataset F's split search used cap=None, i.e. it keeps E's existing 60-cap and never adds frames. Source-B non_malignant (PUNLMP) frames are excluded entirely, so all 4,555 source-B images in F are malignant or non_roi.

Patient ID normalization (unchanged from E)

Source Original ID field Normalized range Count
B patient_id (0-29) 1-30 27 (3 PUNLMP-only patients dropped)
C pid 31-190 160
D case_id (0-25) 191-212 22

Label Derivation

Identical to Dataset E. See E's dataset card for the full per-source mapping. The derived 3-class task label is:

subclass_label target3
malignant (0) malignant (0)
non_malignant (1) non_malignant (1)
normal (2) non_roi (2)
landmark (3) non_roi (2)
foreign_body (4) non_roi (2)

Train / Validation / Test Split

Patient-level stratified split using StratifiedGroupKFold (source x dominant-class stratification) for test, then for train/val, followed by greedy patient-swap refinement that minimises the maximum per-class per-split fill-ratio deviation. Hard constraints: every split contains every class and every source; a source-floor of 4 patients per source in val and test.

A grid search over (seed, per-track cap, source_floor) selected the configuration with the lowest score (score = max_fill_dev + penalties):

  • Split ratio: 70 / 15 / 15 (train / val / test)
  • Best config: cap=None, source_floor=4, seed=17
  • Swaps applied: 12
  • Max fill-ratio deviation: 0.0047
  • No patient appears in more than one split (verified)

Split sizes

Split Images Patients
train 10,067 150
validation 2,157 31
test 2,152 28
Total 14,376 209

Class distribution per split

Class Total Train Val Test
malignant 6,515 (45.3%) 4,562 975 978
non_malignant 355 (2.5%) 249 53 53
non_roi 7,506 (52.2%) 5,256 1,129 1,121

Fill ratios (count / target, ideal = 1.00)

Class Train Val Test
malignant 1.00 1.00 1.00
non_malignant 1.00 1.00 1.00
non_roi 1.00 1.00 1.00

Source x split (patients)

Source Train Val Test
B 19 4 4
C 117 23 20
D 14 4 4

Source x split (images)

Source Train Val Test
B 3,402 447 706
C 5,654 1,224 1,189
D 1,011 486 257

Non-malignant patient coverage

Split Patients Images
train 53 249
validation 12 53
test 12 53

All non_malignant images in F come from sources C and D (source-B non_malignant / PUNLMP is excluded).

5-Fold Cross-Validation (train pool only)

StratifiedGroupKFold on the train split (per-image target3 stratification, patient-level grouping, no patient in multiple folds, seed=15, 5 folds).

Fold Patients Images Malignant Non-mal Non-ROI
0 27 2,008 912 49 1,047
1 30 2,017 912 50 1,055
2 33 1,988 912 49 1,027
3 29 2,055 913 50 1,092
4 31 1,999 913 51 1,035

The cv_fold column is -1 for validation and test patients.

Note on minority-class (non-malignant) fold balance

After PUNLMP exclusion the non_malignant class shrinks to 355 images and remains heavily patient-concentrated: only 53 train patients carry non_malignant frames and a few patients dominate the count. Because cross-validation is patient-grouped (a patient never appears in more than one fold), high-volume patients cannot be split across folds, so the per-fold non-malignant count varies modestly (49-51) while the majority classes stay near-perfectly balanced (malignant spread 912-913; non-ROI spread 1,027-1,092). Practitioners should report per-fold non-malignant counts alongside metrics and prefer the macro-averaged F1 across all five folds over any single fold's score when estimating minority-class performance.

Dataset Statistics

By target3

Malignant Non-malignant Non-ROI
6,515 355 7,506

By subclass label

Malignant Non-malignant Normal Landmark Foreign body
6,515 355 7,044 211 251

By cancer label

Total Cancer Non-cancer
14,376 6,514 7,862

By grade label

High-grade Low-grade Not applicable
2,205 4,309 7,862

By imaging modality

WLI NBI BLC
13,605 321 450

By source

B C D
4,555 8,067 1,754

Features Schema

Column Type Description
image Image Cystoscopy image (decoded as PIL Image on load)
cancer_label ClassLabel non_cancer (0) / cancer (1)
grade_label ClassLabel low_grade (0) / high_grade (1) / not_applicable (2)
subclass_label ClassLabel malignant (0) / non_malignant (1) / normal (2) / landmark (3) / foreign_body (4)
source_dataset string B, C, or D -- original source dataset
original_filename string Filename in the original source dataset
patient_id int32 Normalized sequential patient/case ID (1-212)
imaging_type ClassLabel WLI (0) / NBI (1) / BLC (2)
target3 ClassLabel malignant (0) / non_malignant (1) / non_roi (2) -- 3-class task label
track_id string Source-B video track ID (e.g. 008); NA for sources C and D
cv_fold int64 5-fold CV assignment (0-4) for train patients; -1 for val/test

Loading 

from datasets import load_dataset

ds = load_dataset("milkyroad/F")
print(ds)
# DatasetDict({
#     train: 10,067 images
#     validation: 2,157 images
#     test: 2,152 images
# })

sample = ds["train"][0]
print(sample["target3"])  # 0 (malignant), 1 (non_malignant), or 2 (non_roi)

Provenance

Built from milkyroad/E by:

  1. Dropping all PUNLMP frames (source_dataset == 'B' and target3 == 'non_malignant').
  2. Grid-searching (seed, cap, source_floor) for the patient-level split that minimises the maximum per-class per-split fill-ratio deviation, with hard coverage constraints.
  3. Attaching a 5-fold StratifiedGroupKFold CV assignment (seed=15) to the train pool.

Selected config: cap=None, source_floor=4, seed=17, 12 swaps, max fill-ratio deviation 0.0047.