| --- |
| license: cc-by-nc-sa-4.0 |
| task_categories: |
| - visual-question-answering |
| language: |
| - en |
| tags: |
| - pathology |
| - histopathology |
| - benchmark |
| - nucleus-counting |
| pretty_name: PathoVernier |
| size_categories: |
| - n<1K |
| configs: |
| - config_name: default |
| data_files: |
| - split: test |
| path: pathovernier.jsonl |
| extra_gated_prompt: >- |
| PathoVernier is released for non-commercial research use under CC BY-NC-SA 4.0. The questions and |
| reference counts are derived from Lizard, PUMA, PanNuke, CoNSeP and NuCLS; images are not |
| redistributed and must be obtained from the original providers under their own terms. Access |
| requests are reviewed manually. |
| extra_gated_fields: |
| Full name: text |
| Affiliation: text |
| Country: country |
| Intended use: text |
| I will use PathoVernier for non-commercial research only and follow the terms of the source datasets: checkbox |
| I will not use PathoVernier or models evaluated on it for clinical decision-making: checkbox |
| --- |
| |
| # PathoVernier |
|
|
| PathoVernier is a benchmark for quantitative cell-composition reasoning on H&E histopathology patches. |
| Each question requires counting specific nucleus types in specific image regions and deriving an |
| answer from those counts with a deterministic rule. Reference counts come from expert nucleus |
| annotations, so a model's reported counts can be checked in addition to its final answer. |
|
|
| - **759 questions** on **553** 256x256 patches, after review by four board-certified pathologists |
| (27 of 786 candidates removed). |
| - Evaluation code: https://github.com/ChyaZhang/ASPECT |
| - Paper: [https://arxiv.org/abs/2609.34277](https://arxiv.org/abs/2609.34277) |
|
|
| ## Tasks |
|
|
| | Task (`skill`) | Question | |
| |---|---| |
| | Region selection (`argmax_region`) | Which equal-area region has the highest density of a nucleus type? | |
| | Region comparison (`region_compare`) | How does one nucleus type compare between two equal-area regions (much fewer / comparable / much more)? | |
| | Cell-type comparison (`type_compare`) | How do two nucleus types compare over the whole patch? | |
| | Multi-step composition (`multi_hop`) | Select the region with the highest density of a type, then give that type's proportion band there. | |
|
|
| Regions are quadrants, four horizontal bands or four vertical bands. Comparison categories: much fewer |
| if r <= 0.5, much more if r >= 2.0, comparable otherwise (r = a/b; much more if b = 0 and a > 0). |
| Proportion thresholds are stated in each question. |
|
|
| | Source | Region selection | Region comparison | Cell-type comparison | Multi-step | Total | |
| |---|---:|---:|---:|---:|---:| |
| | Lizard | 82 | 85 | 135 | 98 | 400 | |
| | PUMA | 45 | 47 | 10 | 40 | 142 | |
| | PanNuke | 35 | 36 | 33 | 35 | 139 | |
| | CoNSeP | 17 | 7 | 13 | 7 | 44 | |
| | NuCLS | 11 | 11 | 2 | 10 | 34 | |
| | **Total** | 190 | 186 | 193 | 190 | 759 | |
|
|
| ## Files |
|
|
| | File | Content | |
| |---|---| |
| | `pathovernier.jsonl` | one question per line (fields below) | |
| | `patch_index.csv` | source dataset, source image, crop origin `(x0, y0)`, crop size, native image size, pixel size and nucleus count of each patch | |
| | `reconstruct_images.py` | rebuilds the 553 patches from the original datasets | |
|
|
| Fields of `pathovernier.jsonl`: `item_id`, `patch_id`, `dataset`, `organ`, `skill`, `partition`, |
| `region`, `asked` (queried nucleus type or type pair), `question`, `options`, `answer`, `quantities` |
| (reference counts required by the question; multi-step questions also give `_num` / `_den` for the |
| proportion), `n_nuclei`, `extent_um` (field of view). |
|
|
| ## Images |
|
|
| Images are not redistributed. Download the source datasets and run: |
|
|
| ```bash |
| python reconstruct_images.py --lizard <Lizard> --pannuke <PanNuke> --consep <CoNSeP> \ |
| --puma <PUMA> --nucls <NuCLS> --out images/ |
| ``` |
|
|
| Each argument is the extracted official release; source images are located by file name. The script |
| writes `images/<dataset>/<patch_id>.png` (256x256 crops at native resolution), the layout used by the |
| evaluation code. |
|
|
| | Source | License | Used subset | |
| |---|---|---| |
| | Lizard | CC BY-NC-SA 4.0 | DPath, CRAG and GlaS images | |
| | PUMA | CC0 1.0 | nuclei and tissue ROIs | |
| | PanNuke | CC BY-NC-SA 4.0 | folds 1-3 | |
| | CoNSeP | provider terms, non-commercial research | train and test | |
| | NuCLS | provider terms | FOVs with at most 10% unlabeled nuclei | |
|
|
| ## Metrics |
|
|
| - **Acc**: final-answer accuracy over all 759 questions. |
| - **CA**: agreement between the decision derived from the model's reported counts and the reference |
| answer, over questions where that decision is determinable (multi-step: region only). |
| - **RAWR**: 1 - mean fraction of required counts within tolerance max(1, 0.1q), over correct answers |
| that report all required counts. |
| - **Count Acc**: mean fraction of required counts within tolerance over all 759 questions. |
|
|
| The scorer is `python -m aspect.score_pathovernier` in the GitHub repository. |
|
|
| ## Citation |
|
|
| ```bibtex |
| @article{zhang2026see, |
| title = {See, Measure, and Reason: Learning Visually Grounded Reasoning in Pathology}, |
| author = {Zhang, Chengyang and Zhang, Wenchuan and Li, Bo and Li, Mengran and Liu, Xinyu and Yang, Jiaming and Chen, Jie and Zhang, Zhang and Yi, Yuhao and Bu, Hong and Lv, Jiancheng}, |
| journal = {arXiv preprint arXiv:2609.34277}, |
| year = {2026} |
| } |
| ``` |
|
|
