README.md

metadata

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

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 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