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

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Medical Image Computing and Computer Assisted Intervention – MICCAI 2023 (MICCAI 2023)

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.

H. Kim and Y. Shin—Equal contribution.

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Acknowledgements

This research was supported by Basic Science Research Program through the National Research Foundation of Korea funded by the Ministry of Science and ICT (2021R1A4A1031437, 2022R1A2C2008983, 2021R1C1C2008773), Artificial Intelligence Graduate School Program at Yonsei University [No. 2020-0-01361], the KIST Institutional Program (Project No.2E32271-23-078), and partially supported by the Yonsei Signature Research Cluster Program of 2023 (2023-22-0008).

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Correspondence to Dosik Hwang .

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Kim, H., Shin, Y., Hwang, D. (2023). DiMix: Disentangle-and-Mix Based Domain Generalizable Medical Image Segmentation. In: Greenspan, H., et al. Medical Image Computing and Computer Assisted Intervention – MICCAI 2023. MICCAI 2023. Lecture Notes in Computer Science, vol 14222. Springer, Cham. https://doi.org/10.1007/978-3-031-43898-1_24

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  • DOI: https://doi.org/10.1007/978-3-031-43898-1_24

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