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This code snippet first finds all the JPEG images in the dataset directory and extracts their corresponding labels based on the directory structure. It then defines a series of transforms using MONAI's transform API. These transforms include loading the images, adding a channel dimension (since the images are grayscale), scaling the intensity values to the range [0, 1], and converting the images to PyTorch tensors. Finally, it creates a `Dataset` object using the list of images and labels, and applies the defined transforms. It also creates a `DataLoader` object to load the data in batches during training. With these steps, you've successfully loaded and preprocessed the MedNIST dataset using MONAI, and you're ready to start building your medical imaging models.
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