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--- |
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license: apache-2.0 |
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--- |
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# Dataset Card for MedIAnomaly |
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## Dataset Description |
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**MedIAnomaly** is a benchmark designed to evaluate anomaly detection methods in the medical imaging domain. It provides a standardized evaluation protocol across **seven real-world medical image datasets**, including both **image-level anomaly classification (AnoCls)** and **pixel-level anomaly segmentation (AnoSeg)** tasks. |
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All datasets follow a **one-class training setting**, where **only normal (non-anomalous) images are available in the training set**, and the **test set includes both normal and abnormal cases**. This reflects real-world scenarios where anomalies are rare and not annotated during training. |
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The benchmark includes a total of **seven datasets**, spanning across various imaging modalities (X-ray, MRI, fundus, dermatoscopy, histopathology), and ensures unified data format and preprocessing to support fair and reproducible comparison of anomaly detection methods. |
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## Dataset Source |
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- **Homepage**: [https://github.com/caiyu6666/MedIAnomaly](https://github.com/caiyu6666/MedIAnomaly) |
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- **License**: [Apache License 2.0](http://www.apache.org/licenses/LICENSE-2.0) |
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- **Paper**: Yu Cai et al. _MedIAnomaly: A Comparative Study of Anomaly Detection in Medical Images_, arXiv 2024. |
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## Dataset Structure |
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| Dataset | Modality | Task | ๐<sub>train</sub> | ๐<sub>test</sub> (Normal+Abnormal) | |
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|--------------|-----------------------|-------------------|------------------|----------------------------| |
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| RSNA | Chest X-ray | AnoCls | 3851 | 1000 + 1000 | |
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| VinDr-CXR | Chest X-ray | AnoCls | 4000 | 1000 + 1000 | |
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| Brain Tumor | Brain MRI | AnoCls | 1000 | 600 + 600 | |
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| LAG | Retinal fundus image | AnoCls | 1500 | 811 + 811 | |
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| ISIC2018 | Dermatoscopic image | AnoCls | 6705 | 909 + 603 | |
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| Camelyon16 | Histopathology image | AnoCls | 5088 | 1120 + 1113 | |
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| BraTS2021 | Brain MRI | AnoCls & AnoSeg | 4211 | 828 + 1948 | |
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### Notes on Dataset-Specific Definitions |
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- **RSNA**: Training images are all normal chest X-rays. Test set contains a balanced mix of normal and pneumonia images. |
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- **VinDr-CXR**: Training set consists only of normal chest X-rays. Test set includes both normal and abnormal findings. |
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- **Brain Tumor**: MRI scans. All training samples are healthy brains; test set contains normal and tumor cases. |
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- **LAG**: Retinal fundus images. Training set includes only normal cases; glaucomatous images appear in test set. |
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- **ISIC2018**: One-hot multi-label data. Only images with `NV = 1` and all other labels = 0 are considered **normal**. All others (with any other disease present) are considered **abnormal**. |
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- **Camelyon16**: Histopathological whole-slide patches. Training includes only benign tissue. Abnormal cancerous regions are tested. |
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- **BraTS2021**: Brain MRI for both classification and segmentation. Training includes only normal images. Test set includes tumor cases with segmentation masks. |
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## Example Usage |
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### RSNA |
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```python |
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from datasets import load_dataset |
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dataset = load_dataset("randall-lab/medianomaly", name="rsna", split="train", trust_remote_code=True) |
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# dataset = load_dataset("randall-lab/medianomaly", name="rsna", split="test", trust_remote_code=True) |
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# View a sample |
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example = dataset[0] |
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image = example["image"] |
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label = example["label"] # "normal" or "abnormal" |
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image.show() |
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print(f"Label: {label}") |
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``` |
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### Vin-CXR |
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```python |
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from datasets import load_dataset |
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dataset = load_dataset("randall-lab/medianomaly", name="vincxr", split="train", trust_remote_code=True) |
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# dataset = load_dataset("randall-lab/medianomaly", name="vincxr", split="test", trust_remote_code=True) |
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# View a sample |
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example = dataset[0] |
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image = example["image"] |
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label = example["label"] # "normal" or "abnormal" |
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image.show() |
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print(f"Label: {label}") |
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``` |
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### Brain Tumor |
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```python |
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from datasets import load_dataset |
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dataset = load_dataset("randall-lab/medianomaly", name="braintumor", split="train", trust_remote_code=True) |
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# dataset = load_dataset("randall-lab/medianomaly", name="braintumor", split="test", trust_remote_code=True) |
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# View a sample |
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example = dataset[0] |
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image = example["image"] |
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label = example["label"] # "normal" or "abnormal" |
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image.show() |
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print(f"Label: {label}") |
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``` |
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### LAG |
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```python |
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from datasets import load_dataset |
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dataset = load_dataset("randall-lab/medianomaly", name="lag", split="train", trust_remote_code=True) |
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# dataset = load_dataset("randall-lab/medianomaly", name="lag", split="test", trust_remote_code=True) |
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# View a sample |
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example = dataset[0] |
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image = example["image"] |
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label = example["label"] # "normal" or "abnormal" |
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image.show() |
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print(f"Label: {label}") |
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``` |
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### Camelyon16 |
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```python |
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from datasets import load_dataset |
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dataset = load_dataset("randall-lab/medianomaly", name="camelyon16", split="train", trust_remote_code=True) |
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# dataset = load_dataset("randall-lab/medianomaly", name="camelyon16", split="test", trust_remote_code=True) |
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# View a sample |
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example = dataset[0] |
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image = example["image"] |
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label = example["label"] # "normal" or "abnormal" |
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image.show() |
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print(f"Label: {label}") |
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``` |
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### BraTS2021 |
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```python |
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from datasets import load_dataset |
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# Train |
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dataset = load_dataset("randall-lab/medianomaly", name="brats2021", split="train", trust_remote_code=True) |
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example = dataset[0] |
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image = example["image"] |
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label = example["label"] # "normal" or "abnormal" |
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image.show() |
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print(f"Label: {label}") |
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# Test |
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dataset = load_dataset("randall-lab/medianomaly", name="brats2021", split="test", trust_remote_code=True) |
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example = dataset[828] # >= 828 is abnormal images with seg mask |
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image = example["image"] |
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label = example["label"] # "normal" or "abnormal" |
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anno = example["annotation"] # None if label is 0, seg mask if label is 1 |
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image.show() |
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anno.show() |
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print(f"Label: {label}") |
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``` |
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### ISIC2018 |
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```python |
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from datasets import load_dataset |
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dataset = load_dataset("randall-lab/medianomaly", name="isic2018_task3", split="train", trust_remote_code=True) |
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# dataset = load_dataset("randall-lab/medianomaly", name="isic2018_task3", split="test", trust_remote_code=True) |
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# View a sample |
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example = dataset[0] |
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image = example["image"] |
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label = example["label"] # "normal" or "abnormal" |
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labels = example["labels"] # one-hot multi label for different disease [MEL, NV, BCC, AKIEC, BKL, DF, VASC] |
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# Individual binary class labels (0 or 1) |
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mel_label = example["MEL"] |
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nv_label = example["NV"] |
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bcc_label = example["BCC"] |
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akiec_label = example["AKIEC"] |
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bkl_label = example["BKL"] |
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df_label = example["DF"] |
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vasc_label = example["VASC"] |
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image.show() |
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print(f"Label: {label}") |
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``` |
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If you are using colab, you should update datasets to avoid errors |
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``` |
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pip install -U datasets |
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``` |
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## Citation |
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``` |
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@article{cai2024medianomaly, |
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title={MedIAnomaly: A comparative study of anomaly detection in medical images}, |
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author={Cai, Yu and Zhang, Weiwen and Chen, Hao and Cheng, Kwang-Ting}, |
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journal={arXiv preprint arXiv:2404.04518}, |
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year={2024} |
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} |
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``` |
