---
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](https://franblueee.github.io/torchmil/).

This repository provides an adapted version of the [Prostate cANcer graDe Assessment (PANDA) dataset](https://panda.grand-challenge.org/data/) tailored for **Multiple Instance Learning (MIL)**. It is designed for use with the [`PANDAMILDataset`](https://franblueee.github.io/torchmil/api/datasets/pandamil_dataset/) class from the [**torchmil** library](https://franblueee.github.io/torchmil/). 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](https://panda.grand-challenge.org/data/) 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.