---
license: apache-2.0
task_categories:
- image-classification
language:
- en
pretty_name: RSNAICHMIL
---
# RSNA-ICH - 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 [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.

### About the Original RSNA-ICH Dataset

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. 

### Dataset Description

We have preprocessed the CT scans by computing features for each slice using various feature extractors.

- A **slice** is labeled as positive (`slice_label=1`) if it contains evidence of hemorrhage.
- A **CT scan** is labeled as positive (`label=1`) if it contains at least one positive slice.

This means a CT scan is considered positive if there is any evidence of hemorrhage.

### Directory Structure

After extracting the contents of the `.tar.gz` archives, the following directory structure is expected:

```
root
├── features
│   ├── features_{features}
│   │   ├── ctscan_name1.npy
│   │   ├── ctscan_name2.npy
│   │   └── ...
├── labels
│   ├── ctscan_name1.npy
│   ├── ctscan_name2.npy
│   └── ...
├── slice_labels
│   ├── ctscan_name1.npy
│   ├── ctscan_name2.npy
│   └── ...
└── splits.csv
```

Each `.npy` file corresponds to a single CT scan. The `splits.csv` file defines train/test splits for standardized experimentation.