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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: RSNAICHMIL |
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--- |
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# RSNA-ICH - 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 [RSNA Intra-Cranial Hemorrhage (RSNA-ICH) Detection dataset](https://www.kaggle.com/competitions/rsna-intracranial-hemorrhage-detection) tailored for **Multiple Instance Learning (MIL)**. It is designed for use with the [`RSNAMILDataset`](https://franblueee.github.io/torchmil/api/datasets/rsnamil_dataset/) class from the [**torchmil** library](https://franblueee.github.io/torchmil/). RSNA-ICH 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 RSNA-ICH Dataset |
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The original [RSNA-ICH dataset](https://www.kaggle.com/competitions/rsna-intracranial-hemorrhage-detection) contains head CT scans. The task is to identify whether a CT scan contains acute intracranial hemorrhage and its subtypes. The dataset includes a label for each slice. |
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### Dataset Description |
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We have preprocessed the CT scans by computing features for each slice using various feature extractors. |
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- A **slice** is labeled as positive (`slice_label=1`) if it contains evidence of hemorrhage. |
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- A **CT scan** is labeled as positive (`label=1`) if it contains at least one positive slice. |
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This means a CT scan is considered positive if there is any evidence of hemorrhage. |
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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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├── features |
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│ ├── features_{features} |
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│ │ ├── ctscan_name1.npy |
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│ │ ├── ctscan_name2.npy |
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│ │ └── ... |
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├── labels |
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│ ├── ctscan_name1.npy |
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│ ├── ctscan_name2.npy |
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│ └── ... |
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├── slice_labels |
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│ ├── ctscan_name1.npy |
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│ ├── ctscan_name2.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 CT scan. The `splits.csv` file defines train/test splits for standardized experimentation. |
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