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.

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

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:

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

Coming soon.

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