DCSAU-Net: A Deeper and More Compact Split-Attention U-Net for Medical Image Segmentation

• URL: http://arxiv.org/abs/2202.00972v1
• Date: Wed, 2 Feb 2022 11:36:15 GMT
• Title: DCSAU-Net: A Deeper and More Compact Split-Attention U-Net for Medical Image Segmentation
• Authors: Qing Xu and Wenting Duan and Na He
• Abstract summary: We propose a novel split-attention u-shape network (DCSAU-Net) that extracts useful features using multi-scale combined split-attention and deeper depthwise convolution. As a result, DCSAU-Net displays better performance than other state-of-the-art (SOTA) methods in terms of the mean Intersection over Union (mIoU) and F1-socre.
• Score: 1.1315617886931961
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Image segmentation is a key step for medical image analysis. Approaches based on deep neural networks have been introduced and performed more reliable results than traditional image processing methods. However, many models focus on one medical image application and still show limited abilities to work with complex images. In this paper, we propose a novel deeper and more compact split-attention u-shape network (DCSAU-Net) that extracts useful features using multi-scale combined split-attention and deeper depthwise convolution. We evaluate the proposed model on CVC-ClinicDB, 2018 Data Science Bowl, ISIC-2018 and SegPC-2021 datasets. As a result, DCSAU-Net displays better performance than other state-of-the-art (SOTA) methods in terms of the mean Intersection over Union (mIoU) and F1-socre. More significantly, the proposed model demonstrate better segmentation performance on challenging images.

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