# Hepatobench Eval

> Evaluates pathology foundation models on fine-grained tissue classification of liver cancer patches and whole-slide tumor/non-tumor segmentation. It measures how well models can quantify tissue composition (e.g., fibrosis, necrosis, inflammation) within clinically defined regions to support reproducible digital pathology analysis. Use when the user wants to benchmark on HepatoBench, or asks about evaluating this task. Reports F1-score, Dice coefficient.

- Skill: `qhjqhj00/hepatobench-eval` (Agent Skill)
- Install (CLI): `npx skillmds add qhjqhj00/hepatobench-eval`
- Raw SKILL.md: https://api.skillmd.com/api/skills/qhjqhj00/hepatobench-eval/raw
- Safety review: pending
- Works with: Claude Code, Claude.ai, OpenAI Codex
- Category: Docs & Writing
- Author: qhjqhj00 (https://skillmd.com/u/qhjqhj00)
- Updated: 2026-09-08
- Page: https://skillmd.com/skills/qhjqhj00/hepatobench-eval

---


# hepatobench-eval

> A Digital Pathology Resource for Liver Cancer Quantification with Datasets, Benchmarks, and Tools — Xiao et al. (2026) (arXiv:2604.22858, 2026)

## What this evaluates

Evaluates pathology foundation models on fine-grained tissue classification of liver cancer patches and whole-slide tumor/non-tumor segmentation. It measures how well models can quantify tissue composition (e.g., fibrosis, necrosis, inflammation) within clinically defined regions to support reproducible digital pathology analysis.

## Datasets

- **HepatoBench** — total ?; splits: train (-1), val (-1), test (-1)

## Metrics

- `F1-score` **(primary)** — range: [0, 1]
  - Harmonic mean of precision and recall. Computed per class for the seven tissue categories and macro-averaged across classes for the final score.
- `Dice coefficient` **(primary)** — range: [0, 1]
  - 2 * |A ∩ B| / (|A| + |B|), where A and B are the predicted and ground truth segmentation masks. Measures spatial overlap between predicted and annotated tumor/non-tumor regions.
- `Accuracy` — range: [0, 1]
  - Proportion of correctly classified patches out of the total number of patches.
- `IoU` — range: [0, 1]
  - Intersection over Union: |A ∩ B| / |A ∪ B|, measuring the overlap ratio between predicted and ground truth segmentation masks.

## Input / output format

**Input**: 150×150 pixel image patches at 20× magnification for classification; whole-slide images (WSIs) for segmentation.

**Output**: Per patch: one of seven tissue class labels (TUM, FIB, INF, NEC, NOR, REA, STE). Per WSI: a binary mask delineating tumor vs. non-tumor regions.

## Scoring recipe

```python
def compute_metrics(preds, gold):
    acc = sum(p == g for p, g in zip(preds, gold)) / len(gold)
    f1s = []
    for cls in ['TUM','FIB','INF','NEC','NOR','REA','STE']:
        tp = sum(1 for p,g in zip(preds,gold) if p==cls and g==cls)
        fp = sum(1 for p,g in zip(preds,gold) if p==cls and g!=cls)
        fn = sum(1 for p,g in zip(preds,gold) if p!=cls and g==cls)
        prec = tp/(tp+fp) if (tp+fp)>0 else 0
        rec = tp/(tp+fn) if (tp+fn)>0 else 0
        f1s.append(2*prec*rec/(prec+rec) if (prec+rec)>0 else 0)
    macro_f1 = sum(f1s)/len(f1s)
    inter = np.sum(pred_mask & gold_mask)
    union = np.sum(pred_mask | gold_mask)
    iou = inter/union if union>0 else 0
    dice = 2*inter/(np.sum(pred_mask)+np.sum(gold_mask)) if (np.sum(pred_mask)+np.sum(gold_mask))>0 else 0
    return {'accuracy': acc, 'f1_macro': macro_f1, 'iou': iou, 'dice': dice}
```

## Common pitfalls

- Data splits are strictly performed at the slide/patient level to prevent information leakage from overlapping patches derived from the same WSI.
- WSIs used to derive patches for the classification dataset are explicitly excluded from the test set to avoid confounding the end-to-end pipeline evaluation.
- Inference uses overlapping sliding windows, requiring careful stitching to avoid boundary artifacts that could skew mask continuity and metric scores.

## Evidence (verbatim from paper)

> For the patch-level seven-class classification task, we evaluate model performance using Accuracy (Acc), Precision, Recall and F1-score. For whole-slide image (WSI) region segmentation, we report Intersection-over-Union (IoU) ,Dice coefficient (Dice), Precision, Recall and F1-score as the primary evaluation metrics.

## Citation

```bibtex
@misc{xiao2026hepatobench,
  title={A Digital Pathology Resource for Liver Cancer Quantification with Datasets, Benchmarks, and Tools},
  author={Xiao et al. (2026)},
  year={2026},
  note={arXiv:2604.22858}
}
```

- arXiv: 2604.22858