Pediatric Brain Tumor Wsi Eval

---
name: pediatric-brain-tumor-wsi-eval
description: This benchmark evaluates the ability of weakly supervised multiple instance learning models to classify pediatric brain tumors from whole-slide histopathology images. It probes fine-grained diagnostic discrimination across varying class granularities (2 to 7 classes) under conditions of class imbalance and limited data. Use when the user wants to benchmark on Pediatric brain tumor WSI dataset, or asks about evaluating this task. Reports Macro F1.
metadata:
  skill_kind: dataset_eval
  source_arxiv: 2604.21060
  bibtex_key: [REDACTED]
  confidence: high
---

# pediatric-brain-tumor-wsi-eval

> Clinically-Informed Modeling for Pediatric Brain Tumor Classification from Whole-Slide Histopathology Images — Joakim Nguyen et al. (2026) (arXiv:2604.21060, 2026)

## What this evaluates

This benchmark evaluates the ability of weakly supervised multiple instance learning models to classify pediatric brain tumors from whole-slide histopathology images. It probes fine-grained diagnostic discrimination across varying class granularities (2 to 7 classes) under conditions of class imbalance and limited data.

## Datasets

- **Pediatric brain tumor WSI dataset** — total ?; splits: train (-1), val (-1), test (-1)

## Metrics

- `Macro F1` **(primary)** — range: [0, 1]
  - Unweighted mean of per-class F1-scores. Calculated as 2 * (precision * recall) / (precision + recall) for each diagnostic class, then averaged across all classes regardless of their frequency.
- `Weighted F1` — range: [0, 1]
  - F1-score averaged across classes weighted by their support (number of true instances), explicitly accounting for class imbalance in the dataset.

## Input / output format

**Input**: Whole-slide histopathology images (WSIs) processed into image patches. Patient-level labels are assigned via majority voting of slide-level annotations.

**Output**: Predicted diagnostic class label (e.g., 'Ependymal tumors', 'High-grade brain tumors', 'Non-glial brain tumors', 'Non-neoplastic / normal brain', 'Other low-grade glial tumors', 'Pilocytic astrocytoma', 'DMG H3 mutated', or 'Tumor'/'Non-tumor') or class probabilities.

## Scoring recipe

```python
def compute_macro_f1(y_true, y_pred, classes):
    f1_scores = []
    for c in classes:
        tp = sum(1 for t, p in zip(y_true, y_pred) if t == c and p == c)
        fp = sum(1 for t, p in zip(y_true, y_pred) if t != c and p == c)
        fn = sum(1 for t, p in zip(y_true, y_pred) if t == c and p != c)
        prec = tp / (tp + fp) if (tp + fp) > 0 else 0
        rec = tp / (tp + fn) if (tp + fn) > 0 else 0
        f1 = 2 * prec * rec / (prec + rec) if (prec + rec) > 0 else 0
        f1_scores.append(f1)
    return sum(f1_scores) / len(f1_scores)
```

## Common pitfalls

- Splitting data at the slide level instead of the patient level causes severe data leakage and overly optimistic results due to correlated slides from the same patient.
- Class imbalance is significant; using accuracy or unweighted metrics can mask poor performance on minority tumor subtypes.
- Patient-level labels are derived via majority voting of slide-level predictions, which can introduce noise if slide annotations are inconsistent.

## Evidence (verbatim from paper)

> Model performance was evaluated using comprehensive metrics appropriate for multi-class medical image classification: Per-Class Metrics: For each diagnostic class, we computed precision, recall, and F1-score to assess class-specific discrimination ability. Aggregate Metrics: Overall classification accuracy, macro-averaged precision, recall, and F1-score were calculated to summarize global performance across all classes. Additionally, weighted F1-score was computed to account for class imbalance.

## Citation

```bibtex
@misc{nguyen2026clinically,
  title={Clinically-Informed Modeling for Pediatric Brain Tumor Classification from Whole-Slide Histopathology Images},
  author={Joakim Nguyen et al. (2026)},
  year={2026},
  note={arXiv:2604.21060}
}
```

- arXiv: 2604.21060