---
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](https://huggingface.co/datasets/milkyroad/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

```python
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.