README.md

metadata

configs:
  - config_name: default
    data_files:
      - split: train
        path: data/train-*
      - split: validation
        path: data/validation-*
      - split: test
        path: data/test-*
license: unknown
tags:
  - medical-imaging
  - cystoscopy
  - bladder-cancer
  - cancer-detection
  - histological-grade
pretty_name: Unified Cystoscopy Cancer Detection (Dataset E)
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: int32
  splits:
    - name: train
      num_examples: 10436
    - name: validation
      num_examples: 2244
    - name: test
      num_examples: 2241

Unified Cystoscopy Cancer Detection (Dataset E)

Dataset Description

A unified cystoscopy image dataset assembled from three independent sources, prepared for 3-class deep learning (malignant vs non-malignant vs non-ROI). Every image carries a cancer label, a grade label, a subclass label, and a derived target3 label for the 3-class task.

• Total images (after capping): 14,921
• Unique patients/cases: 212
• 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

Source Datasets

Code	Source	Type	Images (raw)	Images (after cap)	Patients/Cases
B	Cystoscopy video dataset	Video frames (stride 8)	6,519	5,100	30
C	CystoDS	Still cystoscopy images	8,067	8,067	160
D	Kaggle cystoscopy frames	Still cystoscopy frames	1,754	1,754	22

Source B frame capping

Source B video frames are temporally autocorrelated (stride 8 extraction). To reduce redundancy, majority-class frames (malignant + non-ROI) are capped at 60 per (patient, track). Non-malignant frames are always kept. This reduces source B from 6,519 to 5,100 images (cap=60, seed=42).

Patient ID normalization

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

Label Derivation

cancer_label (ClassLabel: non_cancer=0, cancer=1)

Source	Field used	Mapping
D	tissue_type	HGC, LGC -> cancer; NST, NTL -> non_cancer
C	class	Malignant -> cancer; all others -> non_cancer
B	per-frame tumor annotation (y) + histological_type	y=1 (carcinoma patient) -> cancer; y=0 or PUNLMP -> non_cancer

grade_label (ClassLabel: low_grade=0, high_grade=1, not_applicable=2)

Source	Field used	High-grade (1)	Low-grade (0)	Not applicable (2)
D	tissue_type	HGC	LGC	NST, NTL
C	subclass	HighGradePapillary, CIS	LowGradePapillary	all other classes
B	histological_type (patient-level)	pT1HG, pTaHG, pT2, pT2HG	pTaLG, pT1LG	PUNLMP, non-tumor frames (y=0)

subclass_label (ClassLabel: malignant=0, non_malignant=1, normal=2, landmark=3, foreign_body=4)

Source	Native field	Mapping
B	histological_type + y	Carcinoma + y=1 -> malignant; PUNLMP -> non_malignant; y=0 -> normal
C	class	Malignant -> malignant; Non-malignant -> non_malignant; Normal mucosa -> normal; Anatomical landmarks -> landmark; Foreign bodies -> foreign_body
D	tissue_type	HGC, LGC -> malignant; NTL -> non_malignant; NST -> normal

target3 (ClassLabel: malignant=0, non_malignant=1, non_roi=2)

Derived 3-class label for the deep learning task:

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)

imaging_type (ClassLabel: WLI=0, NBI=1, BLC=2)

Source	Original field	Values -> unified
B	light_mode	CLARA + CHROMA -> WLI, white light -> WLI
C	modality	WLC -> WLI, BLC -> BLC
D	imaging_type	WLI -> WLI, NBI -> NBI

Train / Validation / Test Split

Patient-level stratified split using StratifiedGroupKFold (source x dominant-class stratification) + post-split patient swapping to minimize fill-ratio deviation.

• Split ratio: 70 / 15 / 15 (train / val / test)
• Seed: 16
• Swaps applied: 14
• No patient appears in more than one split (verified)

Split sizes

Split	Images	Patients
train	10,436	148
validation	2,244	35
test	2,241	29
Total	14,921	212

Class distribution per split

Class	Total	Train	Val	Test
malignant	6,515 (43.7%)	4,568	982	965
non_malignant	900 (6.0%)	631	135	134
non_roi	7,506 (50.3%)	5,237	1,127	1,142

Fill ratios (count / target, ideal = 1.00)

Class	Train	Val	Test
malignant	1.00	1.00	0.99
non_malignant	1.00	1.00	0.99
non_roi	1.00	1.00	1.01

Source x split (patients)

Source	Train	Val	Test
B	21	4	5
C	115	25	20
D	12	6	4

Source x split (images)

Source	Train	Val	Test
B	3,763	760	577
C	5,821	1,129	1,117
D	852	355	547

Non-malignant patient coverage

Split	Patients	Images
train	55	631
validation	12	135
test	13	134

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).

Fold	Patients	Images	Malignant	Non-mal	Non-ROI
0	26	2,028	914	61	1,053
1	35	2,119	913	170	1,036
2	31	2,026	915	61	1,050
3	26	2,026	913	61	1,052
4	30	2,237	913	278	1,046

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

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

Non-malignant images are heavily patient-concentrated in this dataset: in the train pool, two patients alone account for around 70% of all non-malignant images (274 and 168 images respectively), and only 17 of 148 train patients are non-malignant dominant. Because cross-validation is patient-grouped (a patient never appears in more than one fold), these high-volume patients cannot be split across folds and inevitably land in a single fold each. As a result, two folds carry an elevated non-malignant proportion (~8% and ~12%) while the other three sit at ~3%, against a train-pool baseline of ~6%.

This is an inherent limitation of the dataset's patient-level grouping, not of the splitting algorithm. StratifiedGroupKFold guarantees that every fold's training portion contains non-malignant-dominant patients and keeps the majority classes (malignant, non-ROI) near-perfectly balanced across folds (malignant spread: 913-915; non-ROI spread: 1,036-1,053). 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 (after capping)

By target3

Malignant	Non-malignant	Non-ROI
6,515	900	7,506

By subclass label

Malignant	Non-malignant	Normal	Landmark	Foreign body
6,515	900	7,044	211	251

By cancer label

Total	Cancer	Non-cancer
14,921	6,514	8,407

By grade label

High-grade	Low-grade	Not applicable
2,205	4,309	8,407

By imaging modality

WLI	NBI	BLC
14,150	321	450

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	int32	5-fold CV assignment (0-4) for train patients; -1 for val/test

Loading

from datasets import load_dataset

ds = load_dataset("milkyroad/E")
print(ds)
# DatasetDict({
#     train: 10,436 images
#     validation: 2,244 images
#     test: 2,241 images
# })

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