# Glas Gland Segmentation Eval

> Evaluates a weakly supervised segmentation model's ability to accurately delineate glandular structures in colorectal histopathology images using sparse annotations. It also probes cross-domain generalization across different institutional cohorts with varying staining protocols and scanner characteristics. Use when the user wants to benchmark on GlaS, or asks about evaluating this task. Reports mIoU.

- Skill: `qhjqhj00/glas-gland-segmentation-eval` (Agent Skill)
- Install (CLI): `npx skillmds add qhjqhj00/glas-gland-segmentation-eval`
- Raw SKILL.md: https://api.skillmd.com/api/skills/qhjqhj00/glas-gland-segmentation-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/glas-gland-segmentation-eval

---


# glas-gland-segmentation-eval

> Weakly Supervised Teacher-Student Framework with Progressive Pseudo-mask Refinement for Gland Segmentation — Khan et al. (2026) (arXiv:2603.08605, 2026)

## What this evaluates

Evaluates a weakly supervised segmentation model's ability to accurately delineate glandular structures in colorectal histopathology images using sparse annotations. It also probes cross-domain generalization across different institutional cohorts with varying staining protocols and scanner characteristics.

## Datasets

- **GlaS** — total ?; splits: test (-1)

## Metrics

- `mIoU` **(primary)** — range: percent
  - Mean Intersection over Union: the average IoU score computed per class across all images. IoU = TP / (TP + FP + FN).
- `mDice` — range: percent
  - Mean Dice coefficient: the average Dice score computed per class. Dice = 2*TP / (2*TP + FP + FN).

## Input / output format

**Input**: H&E stained histopathology image patches or whole-slide images.

**Output**: Pixel-wise segmentation mask assigning each pixel to one of four classes: malignant glands, benign glands, poorly differentiated clusters/glands, or background stroma.

## Scoring recipe

```python
def compute_metrics(pred_mask, gt_mask, num_classes=4):
    ious, dices = [], []
    for c in range(num_classes):
        pred_c = (pred_mask == c)
        gt_c = (gt_mask == c)
        intersection = np.logical_and(pred_c, gt_c).sum()
        union = np.logical_or(pred_c, gt_c).sum()
        iou = intersection / union if union > 0 else 0.0
        dice = 2 * intersection / (pred_c.sum() + gt_c.sum()) if (pred_c.sum() + gt_c.sum()) > 0 else 0.0
        ious.append(iou)
        dices.append(dice)
    return np.mean(ious) * 100, np.mean(dices) * 100
```

## Common pitfalls

- Metrics are reported as mean ± standard deviation across five independent training runs with different random seeds; single-run results will not match the paper's values.
- Quantitative evaluation is strictly limited to the GlaS dataset; external cohorts (TCGA-COAD, TCGA-READ, SPIDER) lack pixel-level ground truth and are only assessed qualitatively.
- Performance on SPIDER degrades due to domain shift (staining heterogeneity, lower quality), which may cause fragmented boundaries and false positives if not explicitly noted.

## Evidence (verbatim from paper)

> Metrics are mean Intersection over Union (mIoU) and mean Dice coefficient (mDice), reported as mean ± standard deviation across five independent training runs with different random seeds. Our framework achieves competitive state-of-the-art performance on the GlaS benchmark, achieving an mIoU of 80.10% and an mDice of 89.10%.

## Citation

```bibtex
@misc{khan2026weakly,
  title={Weakly Supervised Teacher-Student Framework with Progressive Pseudo-mask Refinement for Gland Segmentation},
  author={Khan et al. (2026)},
  year={2026},
  note={arXiv:2603.08605}
}
```

- arXiv: 2603.08605