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--- |
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license: apache-2.0 |
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task_categories: |
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- image-classification |
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language: |
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- en |
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pretty_name: PANDAMIL |
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--- |
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# PANDA - Multiple Instance Learning (MIL) |
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*Important.* This dataset is part of the [**torchmil** library](https://franblueee.github.io/torchmil/). |
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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. |
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### About the original PANDA Dataset |
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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. |
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### Dataset Description |
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We have preprocessed the whole-slide images (WSIs) by extracting relevant patches and computing features for each patch using various feature extractors. |
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- A **patch** is labeled as positive (`patch_label=1`) if more than 50% of its pixels are annotated as cancerous. |
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- A **WSI** is labeled as positive (`label=1`) if it contains at least one positive patch. |
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This means a slide is considered positive if there is any evidence of cancerous tissue. |
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### Directory Structure |
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After extracting the contents of the `.tar.gz` archives, the following directory structure is expected: |
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``` |
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root |
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├── patches_{patch_size} |
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│ ├── features |
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│ │ ├── features_{features_name} |
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│ │ │ ├── wsi1.npy |
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│ │ │ ├── wsi2.npy |
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│ │ │ └── ... |
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│ ├── labels |
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│ │ ├── wsi1.npy |
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│ │ ├── wsi2.npy |
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│ │ └── ... |
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│ ├── patch_labels |
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│ │ ├── wsi1.npy |
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│ │ ├── wsi2.npy |
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│ │ └── ... |
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│ ├── coords |
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│ │ ├── wsi1.npy |
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│ │ ├── wsi2.npy |
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│ │ └── ... |
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└── splits.csv |
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``` |
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Each `.npy` file corresponds to a single WSI. The `splits.csv` file defines train/test splits for standardized experimentation. |
