Kidney Histopathology Eval

---
name: kidney-histopathology-eval
description: Evaluates histopathology foundation models on kidney-specific downstream tasks, including tile-level morphological classification, molecular information estimation, and slide-level diagnostic/prognostic inference across diverse staining protocols (H&E, PAS, PASM, IHC). Use when the user wants to benchmark on Kidney Digital Pathology Benchmark, or asks about evaluating this task. Reports Matthews Correlation Coefficient (MCC).
metadata:
  skill_kind: dataset_eval
  source_arxiv: 2603.15967
  bibtex_key: [REDACTED]
  confidence: high
---

# kidney-histopathology-eval

> A Comprehensive Benchmark of Histopathology Foundation Models for Kidney Digital Pathology Images — Kasireddy et al. (2026) (arXiv:2603.15967, 2026)

## What this evaluates

Evaluates histopathology foundation models on kidney-specific downstream tasks, including tile-level morphological classification, molecular information estimation, and slide-level diagnostic/prognostic inference across diverse staining protocols (H&E, PAS, PASM, IHC).

## Datasets

- **Kidney Digital Pathology Benchmark** — total ?; splits: test (-1)

## Metrics

- `Matthews Correlation Coefficient (MCC)` **(primary)** — range: [-1, 1]
  - Measures the quality of binary classifications by accounting for true/false positives and negatives. Formula: (TP*TN - FP*FN) / sqrt((TP+FP)(TP+FN)(TN+FP)(TN+FN)). Ranges from -1 to 1.

## Input / output format

**Input**: Frozen embeddings from Histopathology Foundation Models (HFMs) derived from tile-level or slide-level kidney histopathology images, paired with ground-truth labels for tasks such as glomerulosclerosis classification, GBM spike detection, tubule classification, and inflammation classification.

**Output**: Predicted class labels or regression values generated by linear probing or k-Nearest Neighbors (kNN) classifiers applied to the frozen HFM embeddings.

## Scoring recipe

```python
def compute_mcc(y_true, y_pred):
    tp = sum((y_true == 1) & (y_pred == 1))
    tn = sum((y_true == 0) & (y_pred == 0))
    fp = sum((y_true == 0) & (y_pred == 1))
    fn = sum((y_true == 1) & (y_pred == 0))
    numerator = (tp * tn) - (fp * fn)
    denominator = math.sqrt((tp + fp) * (tp + fn) * (tn + fp) * (tn + fn))
    return numerator / denominator if denominator > 0 else 0.0
# Aggregate across 1000 bootstraps of test-time predictions from repeated stratified group 5-fold CV.
```

## Common pitfalls

- Models are evaluated using both linear probes and k-NN classifiers, which can yield different performance rankings and should not be conflated.
- Performance is reported as bootstrap statistics (median/IQR) rather than single-point estimates, requiring careful interpretation of confidence intervals.
- Tile-level tasks use stratified group k-fold cross-validation to prevent data leakage from the same patient or slide across folds.

## Evidence (verbatim from paper)

> Performance is reported as the Matthews Correlation Coefficient (MCC) statistics (minimum to maximum) across 1000 bootstraps obtained from repeated stratified group five-fold cross-validation runs.

## Citation

```bibtex
@misc{kasireddy2026kidneyhistopathologybenchmark,
  title={A Comprehensive Benchmark of Histopathology Foundation Models for Kidney Digital Pathology Images},
  author={Kasireddy et al. (2026)},
  year={2026},
  note={arXiv:2603.15967}
}
```

- arXiv: 2603.15967