Can I read O2M-UDA: Unsupervised dynamic domain adaptation for one-to-multiple medical image segmentation on EtoBox?

O2M-UDA: Unsupervised dynamic domain adaptation for one-to-multiple medical image segmentation by Ziyue Jiang; Yuting He; Shuai Ye; Pengfei Shao; Xiaomei Zhu; Yi Xu; Yang Chen; Jean-Louis Coatrieux; Shuo Li; Guanyu Yang is a Computer Science article available to read on EtoBox.

What is O2M-UDA: Unsupervised dynamic domain adaptation for one-to-multiple medical image segmentation about?

One-to-multiple medical image segmentation aims to directly test a segmentation model trained with the medical images of a one-domain site on those of a multiple-domain site, suffering from segmentation performance degradation on multiple domains. This process avoids additional annotations and helps improve the application value of the model. However, no successful one-to-multiple unsupervised domain adaptation (O2M-UDA) work has been reported in one-to-multiple medical image segmentation due to its inherent challenges: distribution differences among multiple target domains (among-target differences) caused by different scanning equipment and distribution differences between one source domain and multiple target domains (source–target differences). In this paper, we propose an O2M-UDA framework called dynamic domain adaptation (DyDA), for one-to-multiple medical image segmentation, which has two innovations: (1) dynamic credible sample strategy (DCSS) dynamically extracts credible samples from the target site and iteratively updates their number, thus iteratively expanding the generalization boundary of the model and minimizing the among-target differences; (2) hybrid uncertainty l

Who reads O2M-UDA: Unsupervised dynamic domain adaptation for one-to-multiple medical image segmentation?

Author

Ziyue Jiang; Yuting He; Shuai Ye; Pengfei Shao; Xiaomei Zhu; Yi Xu; Yang Chen; Jean-Louis Coatrieux; Shuo Li; Guanyu Yang

Publisher

Elsevier BV

Published

2023

Language

EN

Field

Computer Science (Physical Sciences)