Deep attention enhanced networks for medical image segmentation

Longfeng Shen; Qiong Wang; Wei Wang; Ran Wang; Yexun Yu; Changchang Zhang

doi:10.1117/12.3029812

21 June 2024 Deep attention enhanced networks for medical image segmentation

Longfeng Shen, Qiong Wang, Wei Wang, Ran Wang, Yexun Yu, Changchang Zhang

Proceedings Volume 13167, International Conference on Remote Sensing, Mapping, and Image Processing (RSMIP 2024); 131671X (2024) https://doi.org/10.1117/12.3029812
Event: International Conference on Remote Sensing, Mapping and Image Processing (RSMIP 2024), 2024, Xiamen, China

Abstract

Medical image segmentation is a crucial task within the realm of medical image processing. Nevertheless, the intrinsic characteristics of medical images and the limited availability of data constrain the model's generalization capacity. Addressing this challenge requires an infusion of more data and the implementation of effective segmentation techniques to enhance model performance. In response to this need, we propose a deep attention enhanced network for medical image segmentation. This innovative approach boosts the segmentation model's efficacy through techniques such as data augmentation, a deep attention enhanced decoder, and a dual convolutional segmentation head. Validation across multiple datasets substantiates the method's effectiveness and its robust generalization capabilities.

(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Longfeng Shen, Qiong Wang, Wei Wang, Ran Wang, Yexun Yu, and Changchang Zhang "Deep attention enhanced networks for medical image segmentation", Proc. SPIE 13167, International Conference on Remote Sensing, Mapping, and Image Processing (RSMIP 2024), 131671X (21 June 2024); https://doi.org/10.1117/12.3029812

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KEYWORDS

Image segmentation

Data modeling

Medical imaging

Convolution

Image enhancement

Transformers

Education and training

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