Hybrid dilation and attention residual U-Net for medical image segmentation

江苏省应用数学（中国矿业大学）中心系列学术报告

报告题目：Hybrid dilation and attention residual U-Net for medical image segmentation

报告人：王泽坤 博士 单位：江西科技师范大学

报告时间：2025年12月6日（周六）上午 10:50--11:40

报告地点：数学学院A321

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报告人简介：王泽坤，博士，江西科技师范大学讲师。2025年毕业于南昌大学，获理学博士学位。已发表SCI论文6篇，拥有公开专利2项，南昌大学2025届“优秀毕业研究生”。目前主要研究方向为聚类，矩阵方程，深度学习，图像处理。

Abstract: Medical image segmentation is a typical task in medical image processing and critical foundation in medical image analysis. U-Net is well-liked in medical image segmentation, but it doesn’t fully explore useful features of the channel and capitalize on the contextual information. Therefore, we present an improved U-Net with residual connections, adding a plug-and-play, very portable channel attention (CA) block and a hybrid dilated attention convolutional (HDAC) layer to perform medical image segmentation for different tasks accurately and effectively, and call it HDA-ResUNet, in which we fully utilize advantages of U-Net, attention mechanism and dilated convolution. In contrast to the simple copy splicing of U-Net in the skip connection, the channel attention block is inserted into the extracted feature map of the encoding path before decoding operation. Since this block is lightweight, we can apply it to multiple layers in the backbone network to optimize the channel effect of this layer’s coding operation. In addition, the convolutional layer at the bottom of the “U"-shaped network is replaced by a hybrid dilated attention convolutional (HDAC) layer to fuse information from different sizes of receptive fields. The proposed HDA-ResUNet is evaluated on four datasets: liver and tumor segmentation (LiTS 2017), lung segmentation (Lung dataset), nuclear segmentation in microscope images (DSB 2018) and neuron structure segmentation (ISBI 2012). The dice global scores of liver and tumor segmentation (LiTS 2017) reach 0.949 and 0.799. The dice coefficients of lung segmentation and nuclear segmentation are 0.9797 and 0.9081 respectively, and the information theoretic score for the last one is 0.9703. The segmentation results are all more accurate than U-Net with fewer parameters, and the problem of slow convergence speed of U-Net on DBS 2018 is solved.