Dataset Card for MedIAnomaly

Dataset Description

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.

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.

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.

Dataset Source

• Homepage: https://github.com/caiyu6666/MedIAnomaly
• License: Apache License 2.0
• Paper: Yu Cai et al. MedIAnomaly: A Comparative Study of Anomaly Detection in Medical Images, arXiv 2024.

Dataset Structure

Dataset	Modality	Task	𝒟train	𝒟test (Normal+Abnormal)
RSNA	Chest X-ray	AnoCls	3851	1000 + 1000
VinDr-CXR	Chest X-ray	AnoCls	4000	1000 + 1000
Brain Tumor	Brain MRI	AnoCls	1000	600 + 600
LAG	Retinal fundus image	AnoCls	1500	811 + 811
ISIC2018	Dermatoscopic image	AnoCls	6705	909 + 603
Camelyon16	Histopathology image	AnoCls	5088	1120 + 1113
BraTS2021	Brain MRI	AnoCls & AnoSeg	4211	828 + 1948

Notes on Dataset-Specific Definitions

• RSNA: Training images are all normal chest X-rays. Test set contains a balanced mix of normal and pneumonia images.
• VinDr-CXR: Training set consists only of normal chest X-rays. Test set includes both normal and abnormal findings.
• Brain Tumor: MRI scans. All training samples are healthy brains; test set contains normal and tumor cases.
• LAG: Retinal fundus images. Training set includes only normal cases; glaucomatous images appear in test set.
• 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.
• Camelyon16: Histopathological whole-slide patches. Training includes only benign tissue. Abnormal cancerous regions are tested.
• BraTS2021: Brain MRI for both classification and segmentation. Training includes only normal images. Test set includes tumor cases with segmentation masks.

Example Usage

RSNA

from datasets import load_dataset

dataset = load_dataset("randall-lab/medianomaly", name="rsna", split="train", trust_remote_code=True)
# dataset = load_dataset("randall-lab/medianomaly", name="rsna", split="test", trust_remote_code=True)

# View a sample
example = dataset[0]
image = example["image"]
label = example["label"]  # "normal" or "abnormal"

image.show()
print(f"Label: {label}")

Vin-CXR

from datasets import load_dataset

dataset = load_dataset("randall-lab/medianomaly", name="vincxr", split="train", trust_remote_code=True)
# dataset = load_dataset("randall-lab/medianomaly", name="vincxr", split="test", trust_remote_code=True)

# View a sample
example = dataset[0]
image = example["image"]
label = example["label"]  # "normal" or "abnormal"

image.show()
print(f"Label: {label}")

Brain Tumor

from datasets import load_dataset

dataset = load_dataset("randall-lab/medianomaly", name="braintumor", split="train", trust_remote_code=True)
# dataset = load_dataset("randall-lab/medianomaly", name="braintumor", split="test", trust_remote_code=True)

# View a sample
example = dataset[0]
image = example["image"]
label = example["label"]  # "normal" or "abnormal"

image.show()
print(f"Label: {label}")

LAG

from datasets import load_dataset

dataset = load_dataset("randall-lab/medianomaly", name="lag", split="train", trust_remote_code=True)
# dataset = load_dataset("randall-lab/medianomaly", name="lag", split="test", trust_remote_code=True)

# View a sample
example = dataset[0]
image = example["image"]
label = example["label"]  # "normal" or "abnormal"

image.show()
print(f"Label: {label}")

Camelyon16

from datasets import load_dataset

dataset = load_dataset("randall-lab/medianomaly", name="camelyon16", split="train", trust_remote_code=True)
# dataset = load_dataset("randall-lab/medianomaly", name="camelyon16", split="test", trust_remote_code=True)

# View a sample
example = dataset[0]
image = example["image"]
label = example["label"]  # "normal" or "abnormal"

image.show()
print(f"Label: {label}")

BraTS2021

from datasets import load_dataset

# Train
dataset = load_dataset("randall-lab/medianomaly", name="brats2021", split="train", trust_remote_code=True)

example = dataset[0]
image = example["image"]
label = example["label"]  # "normal" or "abnormal"

image.show()
print(f"Label: {label}")

# Test
dataset = load_dataset("randall-lab/medianomaly", name="brats2021", split="test", trust_remote_code=True)

example = dataset[828] # >= 828 is abnormal images with seg mask
image = example["image"]
label = example["label"]  # "normal" or "abnormal"
anno = example["annotation"] # None if label is 0, seg mask if label is 1

image.show()
anno.show()
print(f"Label: {label}")

ISIC2018

from datasets import load_dataset

dataset = load_dataset("randall-lab/medianomaly", name="isic2018_task3", split="train", trust_remote_code=True)
# dataset = load_dataset("randall-lab/medianomaly", name="isic2018_task3", split="test", trust_remote_code=True)

# View a sample
example = dataset[0]
image = example["image"]
label = example["label"]  # "normal" or "abnormal"
labels = example["labels"] # one-hot multi label for different disease [MEL, NV, BCC, AKIEC, BKL, DF, VASC]

# Individual binary class labels (0 or 1)
mel_label = example["MEL"]
nv_label = example["NV"]
bcc_label = example["BCC"]
akiec_label = example["AKIEC"]
bkl_label = example["BKL"]
df_label = example["DF"]
vasc_label = example["VASC"]

image.show()
print(f"Label: {label}")

If you are using colab, you should update datasets to avoid errors

pip install -U datasets

Citation

@article{cai2024medianomaly,
  title={MedIAnomaly: A comparative study of anomaly detection in medical images},
  author={Cai, Yu and Zhang, Weiwen and Chen, Hao and Cheng, Kwang-Ting},
  journal={arXiv preprint arXiv:2404.04518},
  year={2024}
}