license: apache-2.0
task_categories:
- image-classification
language:
- en
pretty_name: PANDAMIL
PANDA - Multiple Instance Learning (MIL)
Important. This dataset is part of the torchmil library.
This repository provides an adapted version of the Prostate cANcer graDe Assessment (PANDA) dataset tailored for Multiple Instance Learning (MIL). It is designed for use with the PANDAMILDataset class from the torchmil library. PANDA is a widely used benchmark in MIL research, making this adaptation particularly valuable for developing and evaluating MIL models.
About the original PANDA Dataset
The original PANDA dataset contains WSIs of hematoxylin and eosin (H&E) stained prostate biopsy samples. The task is to classify the severity of prostate cancer within each slide, and to localize the cancerous tissue precisely. The dataset includes high-quality pixel-level annotations marking the cancerous tissue.
Dataset Description
We have preprocessed the whole-slide images (WSIs) by extracting relevant patches and computing features for each patch using various feature extractors.
- A patch is labeled as positive (
patch_label=1) if more than 50% of its pixels are annotated as cancerous. - A WSI is labeled as positive (
label=1) if it contains at least one positive patch.
This means a slide is considered positive if there is any evidence of cancerous tissue.
Directory Structure
After extracting the contents of the .tar.gz archives, the following directory structure is expected:
root
├── patches_{patch_size}
│ ├── features
│ │ ├── features_{features_name}
│ │ │ ├── wsi1.npy
│ │ │ ├── wsi2.npy
│ │ │ └── ...
│ ├── labels
│ │ ├── wsi1.npy
│ │ ├── wsi2.npy
│ │ └── ...
│ ├── patch_labels
│ │ ├── wsi1.npy
│ │ ├── wsi2.npy
│ │ └── ...
│ ├── coords
│ │ ├── wsi1.npy
│ │ ├── wsi2.npy
│ │ └── ...
└── splits.csv
Each .npy file corresponds to a single WSI. The splits.csv file defines train/test splits for standardized experimentation.