Histovit Cancer Classification Eval

---
name: histovit-cancer-classification-eval
description: Multi-class histopathological image classification for cancer diagnosis across four tissue types (breast, prostate, bone, cervical). It probes the model's ability to extract robust morphological features from stained whole-slide image tiles without data augmentation. Use when the user wants to benchmark on ICIAR2018, SIPAkMeD, SICAPv2, UT-Osteosarcoma, or asks about evaluating this task. Reports accuracy.
metadata:
  skill_kind: dataset_eval
  source_arxiv: 2508.11181
  bibtex_key: ahmed2025histovit
  confidence: high
---

# histovit-cancer-classification-eval

> HistoViT: Vision Transformer for Accurate and Scalable Histopathological Cancer Diagnosis — Ahmed et al. (2025) (arXiv:2508.11181, 2025)

## What this evaluates

Multi-class histopathological image classification for cancer diagnosis across four tissue types (breast, prostate, bone, cervical). It probes the model's ability to extract robust morphological features from stained whole-slide image tiles without data augmentation.

## Datasets

- **ICIAR2018** — total 11794; splits: train (-1), test (-1)
- **SIPAkMeD** — total 4049; splits: train (-1), test (-1)
- **SICAPv2** — total 11836; splits: train (-1), test (-1)
- **UT-Osteosarcoma** — total 10017; splits: train (-1), test (-1)

## Metrics

- `accuracy` **(primary)** — range: [0, 1]
  - Fraction of correctly classified samples out of the total number of samples in the test set.
- `precision` — range: [0, 1]
  - Ratio of true positive predictions to the total number of positive predictions for each class, typically macro-averaged in multi-class settings.
- `recall` — range: [0, 1]
  - Ratio of true positive predictions to the total number of actual positives for each class, typically macro-averaged in multi-class settings.
- `AUC` — range: [0, 1]
  - Area Under the Receiver Operating Characteristic Curve, computed using a one-vs-rest strategy for multiclass classification.

## Input / output format

**Input**: RGB histopathological image tiles/patches, resized to 224×224 pixels, with pixel values normalized to [0, 1].

**Output**: Class label prediction from a fixed set of C classes.

## Scoring recipe

```python
def compute_metrics(y_true, y_pred, y_prob, num_classes):
    accuracy = (y_true == y_pred).mean()
    precision = precision_score(y_true, y_pred, average='macro')
    recall = recall_score(y_true, y_pred, average='macro')
    auc = roc_auc_score(y_true, y_prob, multi_class='ovr', average='macro')
    return accuracy, precision, recall, auc
```

## Common pitfalls

- No data augmentation is used, which is atypical for deep learning on small medical datasets and may limit generalizability.
- Split strategies vary by dataset (80:20, 70:30, 5-fold CV), making direct cross-dataset comparison difficult.
- AUC is computed using a one-vs-rest strategy for multiclass classification, not macro/micro averaging.

## Evidence (verbatim from paper)

> Compute test loss and accuracy metrics ... One-vs-rest AUC curve for one-vs-rest multiclass classification using the proposed model across three different cancer types

## Citation

```bibtex
@misc{ahmed2025histovit,
  title={HistoViT: Vision Transformer for Accurate and Scalable Histopathological Cancer Diagnosis},
  author={Ahmed et al. (2025)},
  year={2025},
  note={arXiv:2508.11181}
}
```

- arXiv: 2508.11181