dellacorte/PANDA-PLUS-Bench
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)… See the full description on the dataset page: https://huggingface.co/datasets/dellacorte/PANDA-PLUS-Bench.
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
Usage
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 identifierEvaluation
See our Colab notebook for standardized evaluation.
Citation
@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
