Multi Cancer Histopathology Eval

---
name: multi-cancer-histopathology-eval
description: Evaluates deep learning models on their ability to classify multi-type cancer histopathological images across six distinct cancer categories. It probes the model's capacity to learn discriminative morphological features and generalize across heterogeneous medical imaging conditions. Use when the user wants to benchmark on Multi-Cancer Histopathology Dataset (Kaggle), or asks about evaluating this task. Reports accuracy.
metadata:
  skill_kind: dataset_eval
  source_arxiv: 2604.09468
  bibtex_key: khan2026dsvtla
  confidence: high
---

# multi-cancer-histopathology-eval

> DSVTLA: Deep Swin Vision Transformer-Based Transfer Learning Architecture for Multi-Type Cancer Histopathological Cancer Image Classification — Khan et al. (2026) (arXiv:2604.09468, 2026)

## What this evaluates

Evaluates deep learning models on their ability to classify multi-type cancer histopathological images across six distinct cancer categories. It probes the model's capacity to learn discriminative morphological features and generalize across heterogeneous medical imaging conditions.

## Datasets

- **Multi-Cancer Histopathology Dataset (Kaggle)** — total ?; splits: train (-1), val (-1), test (-1)

## Metrics

- `accuracy` **(primary)** — range: percent
  - Standard classification accuracy: the proportion of correctly classified instances out of the total number of instances in the test set.
- `precision` — range: percent
  - Ratio of true positive predictions to the total number of positive predictions for each class.
- `recall` — range: percent
  - Ratio of true positive predictions to the total number of actual positives for each class.
- `F1-score` — range: percent
  - Harmonic mean of precision and recall, providing a single metric that balances both concerns.

## Input / output format

**Input**: Histopathological images of cancer tissue (e.g., breast, oral, lung, colon, kidney, acute lymphocytic leukemia).

**Output**: Predicted cancer class label.

## Scoring recipe

```python
def compute_metrics(predictions, gold_labels):
    correct = sum(1 for p, g in zip(predictions, gold_labels) if p == g)
    accuracy = correct / len(gold_labels)
    # Precision, Recall, F1 computed per class using standard sklearn metrics
    return accuracy, precision, recall, f1
```

## Common pitfalls

- Dataset size and exact train/val/test split ratios are not reported, making reproducibility difficult.
- Multiple models report 100% accuracy on certain cancer types (e.g., lung, colon, leukemia), which may indicate overly simple splits or data leakage.
- Results are averaged over three runs, but standard deviations or confidence intervals are omitted, obscuring variance.

## Evidence (verbatim from paper)

> We employed a set of widely accepted classification metrics accuracy, precision, recall, F1-score to quantify the performance of our models. These metrics collectively provide a comprehensive understanding of the model's strengths and limitations in terms of prediction correctness, class-wise balance, and error distribution.

## Citation

```bibtex
@misc{khan2026dsvtla,
  title={DSVTLA: Deep Swin Vision Transformer-Based Transfer Learning Architecture for Multi-Type Cancer Histopathological Cancer Image Classification},
  author={Khan et al. (2026)},
  year={2026},
  note={arXiv:2604.09468}
}
```

- arXiv: 2604.09468