💻 computer science
SOA-UNet: Sparse Otsu-guided Attention U-Net with Adaptive Boundary Loss for Lightweight Brain Tumor Segmentation
This paper introduces SOA-UNet, a lightweight deep learning framework that combines sparse feature encoding, Otsu-based attention guidance, and an Adaptive Boundary Loss to achieve accurate, fast, and interpretable brain tumor segmentation from MRI images.
Original paper licensed under CC BY 4.0 (https://creativecommons.org/licenses/by/4.0/). This is an AI-generated explanation of the paper below. It is not written or endorsed by the authors. For technical accuracy, refer to the original paper. Read full disclaimer
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