A Medical Image Segmentation Network with Multi-Scale and Dual-Branch Attention
Source abstract
Abstract
Accurate medical image segmentation can assist doctors in observing lesion areas and making precise judgments. Effectively utilizing important multi-scale semantic information in local and global contexts is key to improving segmentation accuracy. In this paper, we present a multi-scale dual attention network (MSDA-Net), which enhances feature representation under different receptive fields and effectively utilizes the important multi-scale semantic information from both local and global contexts in medical images. MSDA-Net is a typical encoder–decoder structure and introduces a multi-receptive field densely connected module (MRD) in the decoder. This module captures semantic information across various receptive fields and utilizes dense connections to provide comprehensive and detailed semantic representations. Furthermore, a parallel dual-branch attention module (PDA), incorporating spatial and channel attention, focuses intensively on detailed features within lesion areas. This module enhances feature representation, facilitates the identification of disease boundaries, and improves the accuracy of segmentation. To validate the effectiveness of MSDA-Net, we conducted performance analyses on the CVC-ClinicDB, 2018 Data Science Bowl, ISIC 2018, and colon cancer slice datasets. We also compared our method with U-Net, UNet++, and other methods. The experimental results unequivocally demonstrate that MSDA-Net outperforms these methods, showcasing its superior performance in medical image segmentation tasks.
Source: crossref · Quality: baseline
Research context
OpenAlex topics: Radiomics and Machine Learning in Medical Imaging · AI in cancer detection · Advanced Neural Network Applications
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
Affiliations
School of Computer Science, Jiangsu University of Science and Technology, Zhenjiang 212003, China
References (47)
Show source-provided references
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