# Medclipseg Eval

> Evaluates data-efficient and domain-generalizable medical image segmentation using vision-language adaptation. It probes a model's ability to segment anatomical structures and lesions across diverse imaging modalities with limited supervision, while maintaining robustness to out-of-distribution domain shifts and providing calibrated uncertainty estimates. Use when the user wants to benchmark on BUSI, BTMRI, ISIC, Kvasir-SEG, QaTa-COV19, EUS, BUSUC, BUSBRA, BUID, UDIAT, CVC-ColonDB, CVC-ClinicDB, CVC-300, BKAI, BRISC, UWaterlooSkinCancer, or asks about evaluating this task. Reports DSC.

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

---


# medclipseg-eval

> MedCLIPSeg: Probabilistic Vision-Language Adaptation for Data-Efficient and Generalizable Medical Image Segmentation — Koleilat et al. (2026) (arXiv:2602.20423, 2026)

## What this evaluates

Evaluates data-efficient and domain-generalizable medical image segmentation using vision-language adaptation. It probes a model's ability to segment anatomical structures and lesions across diverse imaging modalities with limited supervision, while maintaining robustness to out-of-distribution domain shifts and providing calibrated uncertainty estimates.

## Datasets

- **BUSI** — total ?; splits: train (-1), test (-1)
- **BTMRI** — total ?; splits: train (-1), test (-1)
- **ISIC** — total ?; splits: train (-1), test (-1)
- **Kvasir-SEG** — total ?; splits: train (-1), test (-1)
- **QaTa-COV19** — total ?; splits: train (-1), test (-1)
- **EUS** — total ?; splits: train (-1), test (-1)
- **BUSUC** — total ?; splits: train (-1), test (-1)
- **BUSBRA** — total ?; splits: train (-1), test (-1)
- **BUID** — total ?; splits: train (-1), test (-1)
- **UDIAT** — total ?; splits: train (-1), test (-1)
- **CVC-ColonDB** — total ?; splits: train (-1), test (-1)
- **CVC-ClinicDB** — total ?; splits: train (-1), test (-1)
- **CVC-300** — total ?; splits: train (-1), test (-1)
- **BKAI** — total ?; splits: train (-1), test (-1)
- **BRISC** — total ?; splits: train (-1), test (-1)
- **UWaterlooSkinCancer** — total ?; splits: train (-1), test (-1)

## Metrics

- `DSC` **(primary)** — range: percent
  - Dice Similarity Coefficient: 2 * |A ∩ B| / (|A| + |B|), measuring voxel-wise overlap between prediction and ground truth.
- `NSD` — range: percent
  - Normalized Surface Distance: Average symmetric surface distance between predicted and ground truth contours, normalized by image dimensions.
- `HM DSC` — range: percent
  - Harmonic Mean of In-Distribution (ID) and Out-of-Distribution (OOD) DSC scores, used to balance performance across domain shifts.
- `Brier score` — range: [0, 1]
  - Mean squared difference between predicted uncertainty probabilities and actual binary outcomes, measuring calibration quality.
- `Spearman correlation` — range: [-1, 1]
  - Rank-based correlation coefficient between predicted uncertainty maps and actual segmentation errors.

## Input / output format

**Input**: Medical images paired with textual prompts describing the target anatomy or pathology. If prompts are missing, they are synthetically generated using GPT-5.

**Output**: Pixel-level segmentation masks and continuous per-pixel uncertainty scores.

## Scoring recipe

```python
def compute_dsc(pred_mask, gt_mask):
    intersection = np.sum(pred_mask & gt_mask)
    return 2.0 * intersection / (np.sum(pred_mask) + np.sum(gt_mask))

def compute_nsd(pred_mask, gt_mask):
    # Compute symmetric surface distance between contours
    return np.mean(symmetric_surface_distance(pred_mask, gt_mask))

def compute_hm_dsc(id_dsc, ood_dsc):
    return 2 * (id_dsc * ood_dsc) / (id_dsc + ood_dsc)
```

## Common pitfalls

- Domain generalization evaluation strictly forbids fine-tuning on target datasets; models are trained fully supervised on a single source and tested directly on OOD targets without adaptation.
- Text prompts for datasets lacking clinical annotations are synthetically generated via GPT-5, which may introduce distribution shifts compared to human-written reports.
- Uncertainty calibration metrics (Brier score, Spearman correlation) are computed exclusively over foreground regions, not the full image background.

## Evidence (verbatim from paper)

> We use Dice Similarity Coefficient (DSC) and normalized surface distance (NSD) to compare segmentation accuracy.

## Citation

```bibtex
@misc{koleilat2026medclipseg,
  title={MedCLIPSeg: Probabilistic Vision-Language Adaptation for Data-Efficient and Generalizable Medical Image Segmentation},
  author={Koleilat et al. (2026)},
  year={2026},
  note={arXiv:2602.20423}
}
```

- arXiv: 2602.20423