Datasets:
ArXiv:
License:
| import os | |
| import json | |
| import datasets | |
| import pandas as pd | |
| _DESCRIPTION = """\ | |
| MedIAnomaly is a benchmark for evaluating anomaly detection methods on seven diverse medical imaging datasets: | |
| RSNA, VinCXR, BrainTumor, LAG, ISIC2018_Task3, Camelyon16, and BraTS2021. It supports both image-level | |
| classification and pixel-level segmentation tasks. | |
| All datasets follow a consistent one-class learning protocol: the training set contains only normal (non-anomalous) | |
| images, while the test set includes both normal and abnormal cases. This setting is designed to reflect real-world | |
| scenarios where anomalous samples are rare or unavailable during training. MedIAnomaly provides standardized preprocessing, train/test splits, and label formats to facilitate fair comparison | |
| across methods. | |
| """ | |
| _HOMEPAGE = "https://github.com/caiyu6666/MedIAnomaly/tree/main" | |
| _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} | |
| } | |
| """ | |
| _BASE_URL = "https://huggingface.co/datasets/randall-lab/medianomaly/resolve/main" | |
| _URLS = { | |
| "rsna": f"{_BASE_URL}/rsna.tar", | |
| "brats2021": f"{_BASE_URL}/brats2021.tar", | |
| "braintumor": f"{_BASE_URL}/braintumor.tar", | |
| "camelyon16": f"{_BASE_URL}/camelyon16.tar", | |
| "isic2018": f"{_BASE_URL}/isic2018.tar", | |
| "lag": f"{_BASE_URL}/lag.tar", | |
| "vincxr": f"{_BASE_URL}/vincxr.tar", | |
| } | |
| config_names = {"rsna": "RSNA", "vincxr": "VinCXR", | |
| "brats2021": "BraTS2021", "braintumor": "BrainTumor", | |
| "camelyon16": "Camelyon16", "isic2018_task3": "ISIC2018_Task3", | |
| "lag": "LAG"} | |
| class Medianomaly(datasets.GeneratorBasedBuilder): | |
| BUILDER_CONFIGS = [ | |
| datasets.BuilderConfig(name="rsna", version=datasets.Version("1.0.0"), description="RSNA Pneumonia dataset."), | |
| datasets.BuilderConfig(name="brats2021", version=datasets.Version("1.0.0"), description="BraTS2021 brain tumor dataset."), | |
| datasets.BuilderConfig(name="braintumor", version=datasets.Version("1.0.0"), description="BrainTumor MRI dataset."), | |
| datasets.BuilderConfig(name="camelyon16", version=datasets.Version("1.0.0"), description="Camelyon16 histopathology dataset."), | |
| datasets.BuilderConfig(name="isic2018_task3", version=datasets.Version("1.0.0"), description="ISIC 2018 melanoma classification dataset."), | |
| datasets.BuilderConfig(name="lag", version=datasets.Version("1.0.0"), description="LAG (glaucoma detection) fundus dataset."), | |
| datasets.BuilderConfig(name="vincxr", version=datasets.Version("1.0.0"), description="VinCXR chest X-ray dataset."), | |
| ] | |
| def _info(self): | |
| config_name = self.config.name.lower() | |
| if config_name in ["rsna", "vincxr", "braintumor", "lag", "camelyon16"]: | |
| return datasets.DatasetInfo( | |
| description=_DESCRIPTION, | |
| features=datasets.Features({ | |
| "image": datasets.Image(), | |
| "label": datasets.ClassLabel(names=["normal", "abnormal"]), | |
| }), | |
| supervised_keys=("image", "label"), | |
| homepage=_HOMEPAGE, | |
| license="apache-2.0", | |
| citation=_CITATION, | |
| ) | |
| elif config_name == "brats2021": | |
| return datasets.DatasetInfo( | |
| description=_DESCRIPTION, | |
| features=datasets.Features({ | |
| "image": datasets.Image(), | |
| "label": datasets.ClassLabel(names=["normal", "abnormal"]), | |
| "annotation": datasets.Image(), | |
| }), | |
| supervised_keys=("image", "label"), | |
| homepage=_HOMEPAGE, | |
| license="apache-2.0", | |
| citation=_CITATION, | |
| ) | |
| elif config_name == "isic2018_task3": | |
| return datasets.DatasetInfo( | |
| description=_DESCRIPTION, | |
| features=datasets.Features({ | |
| "image": datasets.Image(), | |
| "labels": datasets.Sequence(datasets.Value("int32")), | |
| "MEL": datasets.ClassLabel(names=["melanoma", "non-melanoma"]), | |
| "NV": datasets.ClassLabel(names=["nevus", "non-nevus"]), | |
| "BCC": datasets.ClassLabel(names=["basal cell carcinoma", "non-basal cell carcinoma"]), | |
| "AKIEC": datasets.ClassLabel(names=["actinic keratosis", "non-actinic keratosis"]), | |
| "BKL": datasets.ClassLabel(names=["benign keratosis", "non-benign keratosis"]), | |
| "VASC": datasets.ClassLabel(names=["vascular lesion", "non-vascular lesion"]), | |
| "DF": datasets.ClassLabel(names=["dermatofibroma", "non-dermatofibroma"]), | |
| }), | |
| supervised_keys=("image", "labels"), | |
| homepage=_HOMEPAGE, | |
| license="apache-2.0", | |
| citation=_CITATION, | |
| ) | |
| else: | |
| raise NotImplementedError(f"{config_name} is not implemented in Medianomaly.") | |
| def _split_generators(self, dl_manager): | |
| config_name = self.config.name.lower() | |
| if config_name not in _URLS: | |
| raise NotImplementedError(f"{config_name} is not implemented in Medianomaly.") | |
| archive_path = dl_manager.download_and_extract(_URLS[config_name]) | |
| if config_name in ["rsna", "vincxr", "braintumor", "lag"]: | |
| data_dir = os.path.join(archive_path, config_names[config_name]) | |
| with open(os.path.join(data_dir, "data.json"), "r") as f: | |
| metadata = json.load(f) | |
| return [ | |
| datasets.SplitGenerator(name=datasets.Split.TRAIN, gen_kwargs={ | |
| "samples": metadata["train"], "base_dir": data_dir, "config": config_name | |
| }), | |
| datasets.SplitGenerator(name=datasets.Split.TEST, gen_kwargs={ | |
| "samples": metadata["test"], "base_dir": data_dir, "config": config_name | |
| }), | |
| ] | |
| elif config_name == "brats2021": | |
| data_dir = os.path.join(archive_path, config_names[config_name]) | |
| return [ | |
| datasets.SplitGenerator(name=datasets.Split.TRAIN, gen_kwargs={ | |
| "samples": "train", "base_dir": data_dir, "config": config_name | |
| }), | |
| datasets.SplitGenerator(name=datasets.Split.TEST, gen_kwargs={ | |
| "samples": "test", "base_dir": data_dir, "config": config_name | |
| }), | |
| ] | |
| elif config_name == "camelyon16": | |
| data_dir = os.path.join(archive_path, config_names[config_name]) | |
| return [ | |
| datasets.SplitGenerator(name=datasets.Split.TRAIN, gen_kwargs={ | |
| "samples": "train", "base_dir": data_dir, "config": config_name | |
| }), | |
| datasets.SplitGenerator(name=datasets.Split.TEST, gen_kwargs={ | |
| "samples": "test", "base_dir": data_dir, "config": config_name | |
| }), | |
| ] | |
| elif config_name == "isic2018_task3": | |
| data_dir = os.path.join(archive_path, config_names[config_name]) | |
| return [ | |
| datasets.SplitGenerator(name=datasets.Split.TRAIN, gen_kwargs={ | |
| "samples": "train", "base_dir": data_dir, "config": config_name | |
| }), | |
| datasets.SplitGenerator(name=datasets.Split.TEST, gen_kwargs={ | |
| "samples": "test", "base_dir": data_dir, "config": config_name | |
| }), | |
| ] | |
| def _generate_examples(self, samples, base_dir, config): | |
| if config in ["rsna", "vincxr", "braintumor", "lag"]: | |
| base_dir = os.path.join(base_dir, "images") | |
| for label_str, items in samples.items(): # only "0" in train, "0"/"1" in test | |
| label = int(label_str) | |
| for idx, item in enumerate(items): | |
| image_path = os.path.join(base_dir, item) | |
| yield idx, { | |
| "image": image_path, | |
| "label": label, | |
| } | |
| elif config == "brats2021": | |
| if samples == "train": | |
| base_dir = os.path.join(base_dir, "train") | |
| for idx, item in enumerate(os.listdir(base_dir)): | |
| image_path = os.path.join(base_dir, item) | |
| yield idx, { | |
| "image": image_path, | |
| "label": 0, # All training images are normal | |
| } | |
| elif samples == "test": | |
| image_dir_normal = os.path.join(base_dir, "test", "normal") | |
| image_dir_tumor = os.path.join(base_dir, "test", "tumor") | |
| annot_dir = os.path.join(base_dir, "test", "annotation") | |
| idx = 0 | |
| for fname in os.listdir(image_dir_normal): | |
| if fname.endswith(".png"): | |
| image_path = os.path.join(image_dir_normal, fname) | |
| yield idx, { | |
| "image": image_path, | |
| "label": 0, | |
| "annotation": None, | |
| } | |
| idx += 1 | |
| for fname in os.listdir(image_dir_tumor): | |
| if fname.endswith(".png"): | |
| image_path = os.path.join(image_dir_tumor, fname) | |
| annot_name = fname.replace("flair", "seg") | |
| annot_path = os.path.join(annot_dir, annot_name) | |
| yield idx, { | |
| "image": image_path, | |
| "label": 1, | |
| "annotation": annot_path, | |
| } | |
| idx += 1 | |
| elif config == "camelyon16": | |
| if samples == "train": | |
| base_dir = os.path.join(base_dir, "train") | |
| base_dir = os.path.join(base_dir, "good") | |
| for idx, item in enumerate(os.listdir(base_dir)): | |
| image_path = os.path.join(base_dir, item) | |
| yield idx, { | |
| "image": image_path, | |
| "label": 0, # All training images are normal | |
| } | |
| elif samples == "test": | |
| base_dir = os.path.join(base_dir, "test") | |
| good_dir = os.path.join(base_dir, "good") | |
| ungood_dir = os.path.join(base_dir, "Ungood") | |
| idx = 0 | |
| for item in os.listdir(good_dir): | |
| if item.endswith(".png"): | |
| image_path = os.path.join(good_dir, item) | |
| yield idx, { | |
| "image": image_path, | |
| "label": 0, | |
| } | |
| idx += 1 | |
| for item in os.listdir(ungood_dir): | |
| if item.endswith(".png"): | |
| image_path = os.path.join(ungood_dir, item) | |
| yield idx, { | |
| "image": image_path, | |
| "label": 1, | |
| } | |
| idx += 1 | |
| elif config == "isic2018_task3": | |
| if samples == "train": | |
| img_dir = os.path.join(base_dir, "ISIC2018_Task3_Training_Input") | |
| label_dir = os.path.join(base_dir, "ISIC2018_Task3_Training_GroundTruth") | |
| label_file = os.path.join(label_dir, "ISIC2018_Task3_Training_GroundTruth.csv") | |
| else: | |
| img_dir = os.path.join(base_dir, "ISIC2018_Task3_Test_Input") | |
| label_dir = os.path.join(base_dir, "ISIC2018_Task3_Test_GroundTruth") | |
| label_file = os.path.join(label_dir, "ISIC2018_Task3_Test_GroundTruth.csv") | |
| df = pd.read_csv(label_file) | |
| for idx, row in df.iterrows(): | |
| image_id = row["image"] | |
| image_path = os.path.join(img_dir, f"{image_id}.jpg") | |
| label_vector = row.iloc[1:].astype(int).tolist() | |
| yield idx, { | |
| "image": image_path, | |
| "labels": label_vector, | |
| "MEL": label_vector[0], | |
| "NV": label_vector[1], | |
| "BCC": label_vector[2], | |
| "AKIEC": label_vector[3], | |
| "BKL": label_vector[4], | |
| "DF": label_vector[5], | |
| "VASC": label_vector[6], | |
| } |