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DiMix: Disentangle-and-Mix Based Domain Generalizable Medical Image Segmentation

Authors
 Hyeongyu Kim  ;  Yejee Shin  ;  Dosik Hwang 
Citation
 Lecture Notes in Computer Science, Vol.14222 : 242-251, 2023-10 
Journal Title
Lecture Notes in Computer Science
ISSN
 0302-9743 
Issue Date
2023-10
Abstract
The rapid advancements in deep learning have revolutionized multiple domains, yet the significant challenge lies in effectively applying this technology to novel and unfamiliar environments, particularly in specialized and costly fields like medicine. Recent deep learning research has therefore focused on domain generalization, aiming to train models that can perform well on datasets from unseen environments. This paper introduces a novel framework that enhances generalizability by leveraging transformer-based disentanglement learning and style mixing. Our framework identifies features that are invariant across different domains. Through a combination of content-style disentanglement and image synthesis, the proposed method effectively learns to distinguish domain-agnostic features, resulting in improved performance when applied to unseen target domains. To validate the effectiveness of the framework, experiments were conducted on a publicly available Fundus dataset, and comparative analyses were performed against other existing approaches. The results demonstrated the power and efficacy of the proposed framework, showcasing its ability to enhance domain generalization performance.
Full Text
https://link.springer.com/chapter/10.1007/978-3-031-43898-1_24
DOI
10.1007/978-3-031-43898-1_24
Appears in Collections:
2. College of Dentistry (치과대학) > Dept. of Oral and Maxillofacial Radiology (영상치의학교실) > 1. Journal Papers
URI
https://ir.ymlib.yonsei.ac.kr/handle/22282913/199352
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