--- 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- 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/.h5 # H-optimus-0 patch features, one HDF5 file per slide ├── val/.h5 └── test/.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//.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.