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аналитика - Ultrasound Image Segmentation
DBF-Net: A Dual-Branch Network with Feature Fusion for Enhanced Ultrasound Image Segmentation by Leveraging Body and Boundary Information
DBF-Net is a novel deep learning architecture that improves the accuracy of ultrasound image segmentation, particularly at lesion boundaries, by fusing information from both body and boundary features.
BRAU-Net: A Novel Deep Learning Approach for Pubic Symphysis-Fetal Head Segmentation in Transperineal Ultrasound Images
This paper introduces BRAU-Net, a novel deep learning architecture that leverages a dynamic sparse attention mechanism and an inverted bottleneck patch expanding module to achieve state-of-the-art results in pubic symphysis-fetal head segmentation on transperineal ultrasound images.
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