Upload medianomaly.py

medianomaly.py

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+import os
+import json
+from PIL import Image
+import datasets
+
+_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",
+}
+
+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):
+        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,
+        )
+    
+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 == "rsna":
+        data_dir = os.path.join(archive_path, 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
+            }),
+        ]
+    
+    
+def _generate_examples(self, samples, base_dir, config):
+   if config == "rsna":
+        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["image"])
+                yield idx, {
+                    "image": image_path,
+                    "label": label,
+                }