LANet for medical image segmentation
https://doi.org/10.21122/2309-4923-2025-1-44-53
Abstract
The paper presents an original LANet model for improving medical image segmentation results based on MobileViT neural network. The developed and integrated Efficient Fusion Attention and Adaptive Feature Fusion blocks improve the quality of feature extraction and reduce data redundancy. The effectiveness of the presented blocks is validated by multiple experiments, including accuracy evaluation on different datasets, based on metrics such as Dice, Precision, Recall, mIoU, model performance evaluation, and ablation study.
About the Authors
Di Zhao
Belarusian State University of Informatics and Radioelectronics
Belarus
Belarus
Di Zhao, Postgraduate at the Department of Information Technologies in Automated Systems
Minsk
Yi Tang
Belarusian State University of Informatics and Radioelectronics
Belarus
Belarus
Yi Tang, Postgraduate at the Department of Information Technologies in Automated Systems
Minsk
D. Y. Pertsau
Belarusian State University of Informatics and Radioelectronics
Belarus
Belarus
Pertsau D., PhD, Associate Professor, Associate Professor of Electronic Computing Machines Department
Minsk
A. B. Gourinovitch
Belarusian State University of Informatics and Radioelectronics
Belarus
Belarus
Gourinovitch A.B., Assistant Professor at the Department of Information Technologies in Automated Systems
Minsk
D. V. Kupryianava
Belarusian State University of Informatics and Radioelectronics
Belarus
Belarus
Kupryianava D., Senior Lecturer, Electronic Computing Machines Department
Minsk
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Review
For citations:
Zhao D., Tang Y., Pertsau D.Y., Gourinovitch A.B., Kupryianava D.V. LANet for medical image segmentation. «System analysis and applied information science». 2025;(1):44-53. (In Russ.) https://doi.org/10.21122/2309-4923-2025-1-44-53
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