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--- |
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license: mit |
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dataset_info: |
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- config_name: classification |
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features: |
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- name: img_path |
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dtype: string |
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- name: bowel |
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dtype: |
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class_label: |
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names: |
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"0": healthy |
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"1": injury |
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- name: extravasation |
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dtype: |
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class_label: |
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names: |
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"0": healthy |
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"1": injury |
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- name: kidney |
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dtype: |
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class_label: |
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names: |
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"0": healthy |
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"1": low |
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"2": high |
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- name: liver |
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dtype: |
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class_label: |
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names: |
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"0": healthy |
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"1": low |
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"2": high |
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- name: spleen |
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dtype: |
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class_label: |
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names: |
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"0": healthy |
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"1": low |
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"2": high |
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- name: any_injury |
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dtype: bool |
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- name: metadata |
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struct: |
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- name: series_id |
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dtype: int32 |
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- name: patient_id |
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dtype: int32 |
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- name: incomplete_organ |
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dtype: bool |
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- name: aortic_hu |
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dtype: float32 |
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- name: pixel_representation |
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dtype: int32 |
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- name: bits_allocated |
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dtype: int32 |
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- name: bits_stored |
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dtype: int32 |
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splits: |
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- name: train |
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num_bytes: 802231 |
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num_examples: 4239 |
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- name: test |
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num_bytes: 89326 |
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num_examples: 472 |
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download_size: 96729254048 |
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dataset_size: 891557 |
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- config_name: classification-with-mask |
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features: |
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- name: img_path |
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dtype: string |
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- name: seg_path |
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dtype: string |
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- name: bowel |
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dtype: |
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class_label: |
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names: |
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"0": healthy |
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"1": injury |
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- name: extravasation |
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dtype: |
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class_label: |
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names: |
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"0": healthy |
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"1": injury |
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- name: kidney |
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dtype: |
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class_label: |
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names: |
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"0": healthy |
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"1": low |
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"2": high |
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- name: liver |
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dtype: |
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class_label: |
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names: |
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"0": healthy |
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"1": low |
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"2": high |
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- name: spleen |
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dtype: |
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class_label: |
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names: |
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"0": healthy |
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"1": low |
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"2": high |
|
- name: any_injury |
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dtype: bool |
|
- name: metadata |
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struct: |
|
- name: series_id |
|
dtype: int32 |
|
- name: patient_id |
|
dtype: int32 |
|
- name: incomplete_organ |
|
dtype: bool |
|
- name: aortic_hu |
|
dtype: float32 |
|
- name: pixel_representation |
|
dtype: int32 |
|
- name: bits_allocated |
|
dtype: int32 |
|
- name: bits_stored |
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dtype: int32 |
|
splits: |
|
- name: train |
|
num_bytes: 58138 |
|
num_examples: 185 |
|
- name: test |
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num_bytes: 6600 |
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num_examples: 21 |
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download_size: 4196738529 |
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dataset_size: 64738 |
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- config_name: segmentation |
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features: |
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- name: img_path |
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dtype: string |
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- name: seg_path |
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dtype: string |
|
- name: metadata |
|
struct: |
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- name: series_id |
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dtype: int32 |
|
- name: patient_id |
|
dtype: int32 |
|
- name: incomplete_organ |
|
dtype: bool |
|
- name: aortic_hu |
|
dtype: float32 |
|
- name: pixel_representation |
|
dtype: int32 |
|
- name: bits_allocated |
|
dtype: int32 |
|
- name: bits_stored |
|
dtype: int32 |
|
splits: |
|
- name: train |
|
num_bytes: 50714 |
|
num_examples: 185 |
|
- name: test |
|
num_bytes: 5757 |
|
num_examples: 21 |
|
download_size: 4196631843 |
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dataset_size: 56471 |
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task_categories: |
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- image-classification |
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- image-segmentation |
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pretty_name: RSNA 2023 Abdominal Trauma Detection (Preprocessed) |
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size_categories: |
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- 1K<n<10K |
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--- |
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# Dataset Card for RSNA 2023 Abdominal Trauma Detection (Preprocessed) |
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## Dataset Description |
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- **Homepage:** [https://huggingface.co/datasets/jherng/rsna-2023-abdominal-trauma-detection](https://huggingface.co/datasets/jherng/rsna-2023-abdominal-trauma-detection) |
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- **Source:** [https://www.kaggle.com/competitions/rsna-2023-abdominal-trauma-detection/data](https://www.kaggle.com/competitions/rsna-2023-abdominal-trauma-detection/data) |
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### Dataset Summary |
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This dataset is the preprocessed version of the dataset from [RSNA 2023 Abdominal Trauma Detection Kaggle Competition](https://www.kaggle.com/competitions/rsna-2023-abdominal-trauma-detection/data). |
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It is tailored for segmentation and classification tasks. It contains 3 different configs as described below: |
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- **classification**: |
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- 4711 instances where each instance includes a CT scan in NIfTI format, target labels, and its relevant metadata. |
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- **segmentation**: |
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- 206 instances where each instance includes a CT scan in NIfTI format, a segmentation mask in NIfTI format, and its relevant metadata. |
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- **classification-with-mask**: |
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- 206 instances where each instance includes a CT scan in NIfTI format, a segmentation mask in NIfTI format, target labels, and its relevant metadata. |
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All CT scans and segmentation masks had already been resampled with voxel spacing (2.0, 2.0, 3.0) and thus its reduced file size. |
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### Usage |
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```python |
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from datasets import load_dataset |
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# Classification dataset |
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rsna_cls_ds = load_dataset("jherng/rsna-2023-abdominal-trauma-detection", streaming=True) # "classification" is the default configuration |
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rsna_cls_ds = load_dataset("jherng/rsna-2023-abdominal-trauma-detection", "classification", streaming=True) # download dataset on-demand and in-memory (no caching) |
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rsna_cls_ds = load_dataset("jherng/rsna-2023-abdominal-trauma-detection", "classification", streaming=False) # download dataset and cache locally (~90.09 GiB) |
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rsna_cls_ds = load_dataset("jherng/rsna-2023-abdominal-trauma-detection", "classification", streaming=True, test_size=0.05, random_state=42) # specify split size for train-test split |
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# Classification dataset with segmentation masks |
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rsna_clsmask_ds = load_dataset("jherng/rsna-2023-abdominal-trauma-detection", "classification-with-mask", streaming=True) # download dataset on-demand and in-memory (no caching) |
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rsna_clsmask_ds = load_dataset("jherng/rsna-2023-abdominal-trauma-detection", "classification-with-mask", streaming=False) # download dataset and cache locally (~3.91 GiB) |
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rsna_clsmask_ds = load_dataset("jherng/rsna-2023-abdominal-trauma-detection", "classification-with-mask", streaming=False, test_size=0.05, random_state=42) # specify split size for train-test split |
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# Segmentation dataset |
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rsna_seg_ds = load_dataset("jherng/rsna-2023-abdominal-trauma-detection", "segmentation", streaming=True) # download dataset on-demand and in-memory (no caching) |
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rsna_seg_ds = load_dataset("jherng/rsna-2023-abdominal-trauma-detection", "segmentation", streaming=False) # download dataset and cache locally (~3.91 GiB) |
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rsna_seg_ds = load_dataset("jherng/rsna-2023-abdominal-trauma-detection", "segmentation", streaming=True, test_size=0.1, random_state=42) # specify split size for train-test split |
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# Get the dataset splits |
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train_rsna_cls_ds = rsna_cls_ds["train"]; test_rsna_cls_ds = rsna_cls_ds["test"] |
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train_rsna_clsmask_ds = rsna_clsmask_ds["train"]; test_rsna_clsmask_ds = rsna_clsmask_ds["test"] |
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train_rsna_seg_ds = rsna_seg_ds["train"]; test_rsna_seg_ds = rsna_seg_ds["test"] |
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# Tip: Download speed up with multiprocessing |
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rsna_cls_ds = load_dataset("jherng/rsna-2023-abdominal-trauma-detection", streaming=False, num_proc=8) # num_proc: num of cpu core used for loading the dataset |
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``` |
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## Dataset Structure |
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### Data Instances |
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#### Configuration 1: classification |
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- **Size of downloaded dataset files:** 90.09 GiB |
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An example of an instance in the 'classification' configuration looks as follows: |
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```json |
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{ |
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"img_path": "https://huggingface.co/datasets/jherng/rsna-2023-abdominal-trauma-detection/resolve/main/train_images/25899/21872.nii.gz", |
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"bowel": 0, |
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"extravasation": 0, |
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"kidney": 0, |
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"liver": 0, |
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"spleen": 0, |
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"any_injury": false, |
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"metadata": { |
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"series_id": 21872, |
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"patient_id": 25899, |
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"incomplete_organ": false, |
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"aortic_hu": 113.0, |
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"pixel_representation": 0, |
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"bits_allocated": 16, |
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"bits_stored": 12 |
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} |
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} |
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``` |
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#### Configuration 2: segmentation |
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- **Size of downloaded dataset files:** 3.91 GiB |
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An example of an instance in the 'segmentation' configuration looks as follows: |
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```json |
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{ |
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"img_path": "https://huggingface.co/datasets/jherng/rsna-2023-abdominal-trauma-detection/resolve/main/train_images/4791/4622.nii.gz", |
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"seg_path": "https://huggingface.co/datasets/jherng/rsna-2023-abdominal-trauma-detection/resolve/main/segmentations/4622.nii.gz", |
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"metadata": { |
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"series_id": 4622, |
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"patient_id": 4791, |
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"incomplete_organ": false, |
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"aortic_hu": 223.0, |
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"pixel_representation": 1, |
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"bits_allocated": 16, |
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"bits_stored": 16 |
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} |
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} |
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``` |
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#### Configuration 3: classification-with-mask |
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- **Size of downloaded dataset files:** 3.91 GiB |
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An example of an instance in the 'classification-with-mask' configuration looks as follows: |
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```json |
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{ |
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"img_path": "https://huggingface.co/datasets/jherng/rsna-2023-abdominal-trauma-detection/resolve/main/train_images/4791/4622.nii.gz", |
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"seg_path": "https://huggingface.co/datasets/jherng/rsna-2023-abdominal-trauma-detection/resolve/main/segmentations/4622.nii.gz", |
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"bowel": 0, |
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"extravasation": 0, |
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"kidney": 0, |
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"liver": 1, |
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"spleen": 1, |
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"any_injury": true, |
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"metadata": { |
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"series_id": 4622, |
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"patient_id": 4791, |
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"incomplete_organ": false, |
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"aortic_hu": 223.0, |
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"pixel_representation": 1, |
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"bits_allocated": 16, |
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"bits_stored": 16 |
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} |
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} |
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``` |
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### Data Fields |
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The data fields for all configurations are as follows: |
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- `img_path`: a `string` feature representing the path to the CT scan in NIfTI format. |
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- `seg_path`: a `string` feature representing the path to the segmentation mask in NIfTI format (only for 'segmentation' and 'classification-with-mask' configurations). |
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- `bowel`, `extravasation`, `kidney`, `liver`, `spleen`: Class label features indicating the condition of respective organs. |
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- `any_injury`: a `bool` feature indicating the presence of any injury. |
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- `metadata`: a dictionary feature containing metadata information with the following fields: |
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- `series_id`: an `int32` feature. |
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- `patient_id`: an `int32` feature. |
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- `incomplete_organ`: a `bool` feature. |
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- `aortic_hu`: a `float32` feature. |
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- `pixel_representation`: an `int32` feature. |
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- `bits_allocated`: an `int32` feature. |
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- `bits_stored`: an `int32` feature. |
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### Data Splits |
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Default split: |
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- 0.9:0.1 with random_state = 42 |
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| Configuration Name | Train (n_samples) | Test (n_samples) | |
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| ------------------------ | ----------------: | ---------------: | |
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| classification | 4239 | 472 | |
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| segmentation | 185 | 21 | |
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| classification-with-mask | 185 | 21 | |
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Modify the split proportion: |
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```python |
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rsna_ds = load_dataset("jherng/rsna-2023-abdominal-trauma-detection", "classification", test_size=0.05, random_state=42) |
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``` |
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## Additional Information |
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### Citation Information |
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- Preprocessed dataset: |
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``` |
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@InProceedings{huggingface:dataset, |
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title = {RSNA 2023 Abdominal Trauma Detection Dataset (Preprocessed)}, |
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author={Hong Jia Herng}, |
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year={2023} |
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} |
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``` |
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- Original dataset: |
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``` |
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@misc{rsna-2023-abdominal-trauma-detection, |
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author = {Errol Colak, Hui-Ming Lin, Robyn Ball, Melissa Davis, Adam Flanders, Sabeena Jalal, Kirti Magudia, Brett Marinelli, Savvas Nicolaou, Luciano Prevedello, Jeff Rudie, George Shih, Maryam Vazirabad, John Mongan}, |
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title = {RSNA 2023 Abdominal Trauma Detection}, |
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publisher = {Kaggle}, |
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year = {2023}, |
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url = {https://kaggle.com/competitions/rsna-2023-abdominal-trauma-detection} |
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} |
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``` |
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