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.
This repository provides an adapted version of the RSNA Intra-Cranial Hemorrhage (RSNA-ICH) Detection dataset tailored for Multiple Instance Learning (MIL). It is designed for use with the RSNAMILDataset class from the torchmil library. 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 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.