README.md

---
license: cc-by-nc-4.0
task_categories:
  - image-feature-extraction
  - visual-question-answering
tags:
  - pathology
  - histology
  - whole-slide-image
  - TCGA
  - foundation-model-features
  - H-optimus-0
  - vision-language
  - multimodal
size_categories:
  - 100B<n<1T
configs:
  - config_name: tier1
    data_files:
      - split: train
        path: questions/tier1/train.jsonl
      - split: validation
        path: questions/tier1/val.jsonl
      - split: test
        path: questions/tier1/test.jsonl
  - config_name: tier2
    data_files:
      - split: train
        path: questions/tier2/train.jsonl
      - split: validation
        path: questions/tier2/val.jsonl
      - split: test
        path: questions/tier2/test.jsonl
pretty_name: CleanSlide — pan-cancer whole-slide vision–language benchmark
extra_gated_prompt: >-
  Please read and agree to the following terms before accessing CleanSlide:
  (1) This resource will be used for scientific research only, and not for any
  commercial or clinical purpose. (2) CleanSlide will be cited in any publication
  that uses this data.
extra_gated_fields:
  Full name: text
  Affiliation / Institution: text
  Country: country
  Intended use of CleanSlide (please describe): text
  I agree to use this dataset for non-commercial research only and to cite CleanSlide: checkbox
extra_gated_button_content: Request access
---

# CleanSlide — pan-cancer whole-slide vision–language benchmark

Official dataset for **CleanSlide**, the largest *public* and cleanest whole-slide-image (WSI) vision–language multiple-choice benchmark on TCGA. This repository is **self-contained**: pre-computed features, the questions, the splits, and a slide-download manifest are all here. Full method, code, and figures live in the **[CleanSlide GitHub repository](https://github.com/wenhaozhang0066/CleanSlide)**.

![CleanSlide is the largest public whole-slide set that is also the cleanest](fig_crossbench.png)

## Abstract

Pathology vision–language models are typically evaluated on TCGA whole-slide VQA, but those benchmarks leak information at two levels — **patient** (slides from one case split across train and test) and **tissue-source-site** (staining/scanner batch shared across cases) — and their questions are often answerable without ever looking at the slide. **CleanSlide** is a contamination-controlled benchmark of **148,654** four-option questions over **9,985** TCGA diagnostic (FFPE) WSIs spanning **32** solid cancer types. Its train / val / test folds are **patient- and site (TSS)-disjoint** (0 overlap), every question is audited on **four cleanliness dimensions** with the blind (image-free) baseline published rather than hidden, and slide features come from **H-optimus-0** (Apache-2.0) — an encoder **not trained on TCGA** — so the released features are redistributable and encoder-uncontaminated.

## Dataset structure

```
features/
├── train/<slide_id>.h5        # H-optimus-0 patch features, one HDF5 file per slide
├── val/<slide_id>.h5
└── test/<slide_id>.h5
questions/
├── tier1/{train,val,test}.jsonl   # Tier 1 — reused & cleaned public TCGA-WSI MCQ
└── tier2/{train,val,test}.jsonl   # Tier 2 — self-generated, text-only MCQ
slides/
├── slides_splits.csv          # slide_id, patient, tss, tumor, fold, split
└── slide_gdc_manifest.csv     # GDC manifest to download the raw diagnostic WSIs
```

- **~480 GB** of features (fp16), one `.h5` per slide, already partitioned by split.
- **148,654** questions total, separated by tier (reuse-and-clean vs self-generated) and by split.
- `slides/` provides the slide → fold/split table and a ready-to-use GDC download manifest for the raw WSIs.

### Feature file format (`.h5`)

| dataset | shape | dtype | meaning |
|---|---|---|---|
| `features` | `[N, 1536]` | float16 | H-optimus-0 embedding per kept patch |
| `coords` | `[N, 2]` | int32 | level-0 (x, y) pixel coordinate of each patch — the **encoder-agnostic canonical layer**: any future encoder can be re-run from these coordinates without re-segmenting/re-tiling |

Per-file attributes: `encoder, mpp_x, target_mpp (0.5 µm/px = 20×), patch_size (224), patch_size_lvl0, level_used, n_patches, tissue_frac_thresh (0.10), mpp_source`. Extraction follows the H-optimus-0 recipe with CLAM tissue detection and the Prov-GigaPath valid-patch rule (**no stain normalization** — the site signature is controlled by the split, not the pixels). Details on [GitHub](https://github.com/wenhaozhang0066/CleanSlide).

## Usage

### Step 1 — download

```python
from huggingface_hub import snapshot_download

# Everything (the features are ~480 GB):
snapshot_download("eric-1w/CleanSlide-features", repo_type="dataset", local_dir="cleanslide")

# Or just the questions + splits (small), skipping the large feature files:
snapshot_download("eric-1w/CleanSlide-features", repo_type="dataset", local_dir="cleanslide",
                  allow_patterns=["questions/*", "slides/*"])
```

### Step 2 — pair a question with its slide features

The `slide` and `split` fields of each question point to `features/<split>/<slide_id>.h5`.

```python
import json, h5py

q = [json.loads(l) for l in open("cleanslide/questions/tier1/test.jsonl")][0]
print(q["question"])
print(q["options"], "->", q["answer"])

with h5py.File(f"cleanslide/features/{q['split']}/{q['slide']}.h5", "r") as f:
    feats  = f["features"][:]   # (N, 1536) float16
    coords = f["coords"][:]     # (N, 2) int32, level-0 pixels
```

### Step 3 — (optional) get the raw slides

The raw WSIs are not redistributed here. To download them, use the included GDC manifest with the [GDC Data Transfer Tool](https://docs.gdc.cancer.gov/Data_Transfer_Tool/Users_Guide/Getting_Started/):

```bash
gdc-client download -m slides/slide_gdc_manifest.csv
```

You can then re-extract features with the CleanSlide pipeline (`code/extract/extract_features.py` on [GitHub](https://github.com/wenhaozhang0066/CleanSlide)).

## A question record

Each line of a `questions/*/*.jsonl` file is one four-option question:

```json
{
  "source": "SlideBench",
  "slide": "TCGA-08-0244-01Z-00-DX1",
  "patient": "TCGA-08-0244",
  "tss": "08",
  "tumor": "GBM",
  "question": "Examine the cellular morphology in the provided whole slide image of glioblastoma. Which cytoplasmic feature is typically observed?",
  "options": ["Mucin production", "Clear cytoplasm", "Fibrillary background", "Granulomatous changes"],
  "answer": "C",
  "answer_type": "mcq",
  "task": "Microscopy",
  "fold": 3,
  "split": "test"
}
```

| field | meaning |
|---|---|
| `source` | origin set — `WSI-Bench`, `SlideBench`, `WSI-VQA`, `CleanSlide-labels`, or `CleanSlide-reports` |
| `slide` / `patient` / `tss` | TCGA slide barcode, patient, and tissue-source-site |
| `tumor` | TCGA study (cancer-type) code, e.g. `GBM` |
| `question` / `options` / `answer` | stem, four options, correct letter (`A`–`D`) |
| `task` | question category (Diagnosis, Microscopy, Subtype, Grading, …) |
| `fold` / `split` | disjoint fold id and `train` / `val` / `test` |

## Citation

If you use CleanSlide, please cite:

```bibtex
@misc{zhang2026cleanslide,
  title        = {CleanSlide: A Leakage-Audited and Shortcut-Controlled Benchmark for Whole-Slide Vision–Language Models},
  author       = {Zhang, Wenhao and others},
  year         = {2026},
  note         = {Manuscript in preparation},
  howpublished = {\url{https://github.com/wenhaozhang0066/CleanSlide}}
}
```

## License & provenance

Questions and derived features: **CC-BY-NC 4.0** (research / non-commercial use only). Underlying TCGA slides follow the NIH/GDC data policy — raw slides are **not** redistributed here. Encoder: H-optimus-0 (Apache-2.0), not trained on TCGA.