Datasets:
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---
license: cc-by-4.0
task_categories:
- image-classification
tags:
- pathology
- histopathology
- prostate-cancer
- gleason-grading
- foundation-models
- benchmark
size_categories:
- 1K<n<10K
dataset_info:
features:
- name: image
dtype: image
- name: label
dtype: int64
- name: slide_id
dtype: string
splits:
- name: baseline
num_bytes: 298170963
num_examples: 3872
- name: color_jitter
num_bytes: 287314003
num_examples: 3872
- name: grayscale
num_bytes: 167680581
num_examples: 3872
- name: gaussian_noise
num_bytes: 458559156
num_examples: 3872
- name: heavy_geometric
num_bytes: 294975593
num_examples: 3872
- name: combined_aggressive
num_bytes: 449136407
num_examples: 3872
- name: macenko_normalization
num_bytes: 303746938
num_examples: 3872
- name: hed_stain_augmentation
num_bytes: 296645574
num_examples: 3872
download_size: 2556068549
dataset_size: 2556229215
configs:
- config_name: default
data_files:
- split: baseline
path: data/baseline-*
- split: color_jitter
path: data/color_jitter-*
- split: grayscale
path: data/grayscale-*
- split: gaussian_noise
path: data/gaussian_noise-*
- split: heavy_geometric
path: data/heavy_geometric-*
- split: combined_aggressive
path: data/combined_aggressive-*
- split: macenko_normalization
path: data/macenko_normalization-*
- split: hed_stain_augmentation
path: data/hed_stain_augmentation-*
---
# PANDA-PLUS-Bench
A benchmark dataset for evaluating WSI-specific feature collapse in pathology foundation models.
## Dataset Description
PANDA-PLUS-Bench contains expert-annotated prostate biopsy patches from 9 whole slide images (9 unique patients) with pixel-level Gleason pattern annotations.
### Dataset Summary
- **Patches**: ~2,770 per augmentation condition
- **Resolution**: 224×224 pixels at 20× magnification
- **Classes**: Benign (0), GP3 (1), GP4 (2), GP5 (3)
- **Slides**: 9 (one per patient)
- **Augmentations**: 8 conditions
### Augmentation Conditions
| Split | Description |
|-------|-------------|
| baseline | ImageNet normalization only |
| color_jitter | Brightness, contrast, saturation, hue |
| grayscale | Complete color removal |
| gaussian_noise | Additive noise (σ=0.05) |
| heavy_geometric | Rotation ±180°, flips |
| combined_aggressive | All augmentations combined |
| macenko_normalization | Stain normalization |
| hed_stain_augmentation | H/E channel perturbation |
## Usage
```python
from datasets import load_dataset
# Load baseline patches
dataset = load_dataset("dellacortelab/PANDA-PLUS-Bench", split="baseline")
# Access a sample
sample = dataset[0]
image = sample['image'] # PIL Image
label = sample['label'] # 0-3
slide_id = sample['slide_id'] # Slide identifier
```
## Evaluation
See our [Colab notebook](https://colab.research.google.com/github/dellacortelab/PANDA-PLUS-Bench/blob/main/PANDA_PLUS_Bench_Evaluation.ipynb) for standardized evaluation.
## Citation
```bibtex
@article{ebbert2025pandaplusbench,
title={PANDA-PLUS-Bench: A Benchmark for Evaluating WSI-Specific Feature Collapse},
author={Ebbert, Joshua and Della Corte, Dennis},
year={2025}
}
```
## License
CC-BY-4.0
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