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README.md

@@ -1,4 +1,14 @@
 ---
+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
@@ -71,3 +81,264 @@ configs:
   - 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.