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_task3": 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(), "label": datasets.ClassLabel(names=["normal", "abnormal"]), "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", "label"), 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") if not os.path.exists(image_path): continue label_vector = row.iloc[1:].astype(int).tolist() yield idx, { "image": image_path, "label": 0 if label_vector == [0, 1, 0, 0, 0, 0, 0] else 1, "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], }