# Sd Mae Histopath Eval

> Evaluates the ability of self-distillation augmented masked autoencoders to learn robust visual representations from histopathological images for downstream tasks like classification, segmentation, and detection, particularly in low-class or cross-domain settings. Use when the user wants to benchmark on PatchCamelyon (PCam), NCT-CRC-HE (NCT), MSIIvsMSS, MoNuSeg, Glas, NuCLS, or asks about evaluating this task. Reports top-1 accuracy.

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

---


# sd-mae-histopath-eval

> Self-distillation Augmented Masked Autoencoders for Histopathological Image Classification — Luo et al. (2022) (arXiv:2203.16983, 2022)

## What this evaluates

Evaluates the ability of self-distillation augmented masked autoencoders to learn robust visual representations from histopathological images for downstream tasks like classification, segmentation, and detection, particularly in low-class or cross-domain settings.

## Datasets

- **PatchCamelyon (PCam)** — total ?; splits: train (-1), val (-1), test (-1)
- **NCT-CRC-HE (NCT)** — total ?; splits: train (-1), val (-1), test (-1)
- **MSIIvsMSS** — total ?; splits: train (-1), val (-1), test (-1)
- **MoNuSeg** — total ?; splits: train (-1), val (-1), test (-1)
- **Glas** — total ?; splits: train (-1), val (-1), test (-1)
- **NuCLS** — total ?; splits: train (-1), val (-1), test (-1)

## Metrics

- `top-1 accuracy` **(primary)** — range: percent
  - Fraction of correctly classified images out of the total number of images in the evaluation set.
- `mIoU` — range: percent
  - Mean Intersection over Union across all semantic classes, computed as the average of IoU per class.
- `box mAP@0.5` — range: percent
  - Mean Average Precision for bounding box detection averaged over recall thresholds at an IoU threshold of 0.5.

## Input / output format

**Input**: Histopathological image patches processed through a Vision Transformer (ViT) encoder with a 0.6 masking ratio during pre-training, and full images/patches during fine-tuning.

**Output**: Class probabilities for classification, pixel-wise segmentation masks for segmentation, and bounding boxes with class scores for object detection.

## Scoring recipe

```python
def compute_metrics(predictions, gold):
    acc = (predictions == gold).mean() * 100
    ious = intersection_over_union(predictions, gold)
    miou = ious.mean() * 100
    ap_scores = compute_ap(predictions, gold, iou_thresh=0.5)
    map50 = ap_scores.mean() * 100
    return {'top-1 accuracy': acc, 'mIoU': miou, 'box mAP@0.5': map50}
```

## Common pitfalls

- Re-dividing datasets instead of strictly following the official train/val/test splits provided by the dataset creators.
- Using inconsistent fine-tuning protocols; the paper mandates a unified fine-tuning approach across all baselines for fair comparison.
- Ignoring high variance in contrastive learning baselines on low-class datasets, which requires multiple runs or careful standard deviation reporting.

## Evidence (verbatim from paper)

> Tab. 1 reports the top-1 accuracy of different methods on the two classification datasets. ... We report box mAP calculated at IoU threshold 0.5 on NuCLS [32].

## Citation

```bibtex
@misc{luo2022selfdistillation,
  title={Self-distillation Augmented Masked Autoencoders for Histopathological Image Classification},
  author={Luo et al. (2022)},
  year={2022},
  note={arXiv:2203.16983}
}
```

- arXiv: 2203.16983