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End-to-end autoencoding architecture for the simultaneous generation of medical images and corresponding segmentation masks
We conduct experiments on two publicly available datasets, MICCAI's Brain Tumor Segmentation Challenge (BRATS), and Head and Neck Tumor Segmentation Challenge (HECKTOR), demonstrating the effectiveness of our method on different medical imaging modalities.
Deep Learning Approaches for Data Augmentation in Medical Imaging: A Review
Our goal is to provide a comprehensive review about the use of deep generative models for medical image augmentation and to highlight the potential of these models for improving the performance of deep learning algorithms in medical image analysis.
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