PDE-UNet: A Modified UNet Architecture Applied to Medical Image Segmentation
DOI:
https://doi.org/10.51408/1963-0139Keywords:
Medical Image Segmentation, Brain Tumor Segmentation, UNet, PDEinspired CNN block, MICCAI BraTS 2020 ChallengeAbstract
Medical image segmentation is a critical task in healthcare, particularly for disease detection and proper treatment planning. Deep learning models achieve high performance in medical image analysis. This paper presents the effectiveness of the new PDE-UNet architecture, inspired by the applications of partial differential equations (PDEs) in neural networks, to enhance medical image segmentation performance. Experiments were conducted on brain tumor MRI images from the BraTS2020 dataset and compared with the traditional UNet architecture.
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