Update medianomaly.py

medianomaly.py

@@ -1,6 +1,7 @@
 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: 
@@ -38,7 +39,7 @@ _URLS = {
 
 config_names = {"rsna": "RSNA", "vincxr": "VinCXR",
                         "brats2021": "BraTS2021", "braintumor": "BrainTumor",
-                        "camelyon16": "Camelyon16", "isic2018": "ISIC2018_Task3",
+                        "camelyon16": "Camelyon16", "isic2018_task3": "ISIC2018_Task3",
                         "lag": "LAG"}
 
 class Medianomaly(datasets.GeneratorBasedBuilder):
@@ -140,6 +141,16 @@ class Medianomaly(datasets.GeneratorBasedBuilder):
                     "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"]:
@@ -225,5 +236,30 @@ class Medianomaly(datasets.GeneratorBasedBuilder):
                         }
                         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],
+                }