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Volume 10
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10.3390/electronics10243103
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Article

Lesion Segmentation Framework Based on Convolutional Neural Networks with Dual Attention Mechanism

by
Fei Xie
1,2,†,
Panpan Zhang
3,†,
Tao Jiang
4,
Jiao She
3,
Xuemin Shen
5,*,
Pengfei Xu
3,*,
Wei Zhao
6,
Gang Gao
7 and
Ziyu Guan
6
1
School of AOAIR, Xidian University, Xi’an 710075, China
2
Department of Electronic Engineering, Tsinghua University, Beijing 100084, China
3
School of Information Science and Technology, Northwest University, Xi’an 710069, China
4
Zhejiang Provincial Seaport, Ningbo 315040, China
5
Department of Oral Mucosal Diseases, Shanghai Ninth People’s Hospital, College of Stomatology, Shanghai Jiao Tong University School of Medicine, Shanghai 200025, China
6
School of Computer Science and Technology, Xidian University, Xi’an 710075, China
7
Shaanxi Great Wisdom Medical Care Technologies Co., Ltd., Xi’an 710075, China
*
Authors to whom correspondence should be addressed.
†
They are the first Co-author.
Electronics 2021, 10(24), 3103; https://doi.org/10.3390/electronics10243103
Submission received: 31 October 2021 / Revised: 29 November 2021 / Accepted: 7 December 2021 / Published: 13 December 2021
(This article belongs to the Special Issue Applications of Computational Intelligence)
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Abstract

Computational intelligence has been widely used in medical information processing. The deep learning methods, especially, have many successful applications in medical image analysis. In this paper, we proposed an end-to-end medical lesion segmentation framework based on convolutional neural networks with a dual attention mechanism, which integrates both fully and weakly supervised segmentation. The weakly supervised segmentation module achieves accurate lesion segmentation by using bounding-box labels of lesion areas, which solves the problem of the high cost of pixel-level labels with lesions in the medical images. In addition, a dual attention mechanism is introduced to enhance the network’s ability for visual feature learning. The dual attention mechanism (channel and spatial attention) can help the network pay attention to feature extraction from important regions. Compared with the current mainstream method of weakly supervised segmentation using pseudo labels, it can greatly reduce the gaps between ground-truth labels and pseudo labels. The final experimental results show that our proposed framework achieved more competitive performances on oral lesion dataset, and our framework further extended to dermatological lesion segmentation.
Keywords: medical image segmentation; computational intelligence; convolutional neural networks; weakly supervised segmentation; attention mechanism medical image segmentation; computational intelligence; convolutional neural networks; weakly supervised segmentation; attention mechanism

Share and Cite

MDPI and ACS Style

Xie, F.; Zhang, P.; Jiang, T.; She, J.; Shen, X.; Xu, P.; Zhao, W.; Gao, G.; Guan, Z. Lesion Segmentation Framework Based on Convolutional Neural Networks with Dual Attention Mechanism. Electronics 2021, 10, 3103. https://doi.org/10.3390/electronics10243103

AMA Style

Xie F, Zhang P, Jiang T, She J, Shen X, Xu P, Zhao W, Gao G, Guan Z. Lesion Segmentation Framework Based on Convolutional Neural Networks with Dual Attention Mechanism. Electronics. 2021; 10(24):3103. https://doi.org/10.3390/electronics10243103

Chicago/Turabian Style

Xie, Fei, Panpan Zhang, Tao Jiang, Jiao She, Xuemin Shen, Pengfei Xu, Wei Zhao, Gang Gao, and Ziyu Guan. 2021. "Lesion Segmentation Framework Based on Convolutional Neural Networks with Dual Attention Mechanism" Electronics 10, no. 24: 3103. https://doi.org/10.3390/electronics10243103

APA Style

Xie, F., Zhang, P., Jiang, T., She, J., Shen, X., Xu, P., Zhao, W., Gao, G., & Guan, Z. (2021). Lesion Segmentation Framework Based on Convolutional Neural Networks with Dual Attention Mechanism. Electronics, 10(24), 3103. https://doi.org/10.3390/electronics10243103

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