TransFuseNetS: A new Image Medical Segmentation Method based on TransFuse Architecture
Authors: 
Wei Chen, Zhiheng WangAuthors Info & Claims
Wei Chen, Zhiheng WangAuthors Info & ClaimsArticle No.: 50, Pages 1 - 11
Abstract
In medical images, image segmentation is a very important method, which can accurately locate and analyze the lesions and tissues. However, due to the complexity of medical images and noise, accurate and robust segmentation is still a difficult problem. In view of the above problems, this project intends to use TransFuseNetS under the framework of Transfuse for image medical segmentation. TransFuseNetS further optimizes and strengthens the Fusion architecture. First of all, this project intends to design a more complex coding architecture and propose an overall attention strategy at the channel level to improve the feature representation and understandability of the image. Secondly, the linear fusion method of two directions is used to make the multi-level images more comprehensive and rich, so as to improve the identification and segmentation of multi-scale images. Through a large number of experiments, we find that TransFuseNetS can effectively improve the recognition rate of CT images. Compared with the traditional artificial neural network, the average error of IOU and ACC of artificial neural network is reduced by 0.1571, and the average error is reduced by 0.0029. The research results of this project will lay a foundation for the research and application in the fields of medical image processing and medical image processing.
Keywords: image medical segmentation; TransFuse; bi-direction linear fusion; encoder-decoder; channel-level global attention mechanism
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Published In
January 2024
506 pages
ISBN:9798400718199
DOI:10.1145/3653804
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Published: 01 June 2024
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CVDL 2024
CVDL 2024: The International Conference on Computer Vision and Deep Learning
January 19 - 21, 2024
Changsha, China
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