Style Enhanced Domain Adaptation Neural Network for Cross-Modality Cervical Tumor Segmentation

Boyun Zheng, Jiahui He, Jiuhe Zhu, Yaoqin Xie, Nazar Zaki, Wenjian Qin

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

Cervical tumor segmentation is an essential step of cervical cancer diagnosis and treatment. Considering that multi-modality data contain more information and are widely available in clinical routine, multi-modality medical image analysis has emerged as a significant field of study. However, annotating tumors for each modality is expensive and time-consuming. Consequently, unsupervised domain adaptation (UDA) has attracted a lot of attention for its ability to achieve excellent performance on unlabeled cross-domain data. Most current UDA methods adapt image translation networks to achieve domain adaptation, however, the generation process may create visual inconsistency and incorrect generation styles due to the instability of generative adversarial networks. Therefore, we propose a novel and efficient method without image translation networks by introducing a style enhancement method into Domain Adversarial Neural Network (DANN)-based model to improve the generalization performance of the shared segmentation network. Experimental results show that our method achieves the best performance on the cross-modality cervical tumor segmentation task compared to current state-of-the-art UDA methods.

Original languageEnglish
Title of host publicationComputational Mathematics Modeling in Cancer Analysis - 2nd International Workshop, CMMCA 2023, Held in Conjunction with MICCAI 2023, Proceedings
EditorsWenjian Qin, Nazar Zaki, Fa Zhang, Jia Wu, Fan Yang, Chao Li
PublisherSpringer Science and Business Media Deutschland GmbH
Pages140-149
Number of pages10
ISBN (Print)9783031450860
DOIs
Publication statusPublished - 2023
Event2nd Workshop on Computational Mathematics Modeling in Cancer Analysis, CMMCA 2023 - Vancouver, Canada
Duration: Oct 8 2023Oct 8 2023

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume14243 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference2nd Workshop on Computational Mathematics Modeling in Cancer Analysis, CMMCA 2023
Country/TerritoryCanada
CityVancouver
Period10/8/2310/8/23

Keywords

  • Cervical tumor segmentation
  • Shuffle Remap
  • Unsupervised domain adaptation

ASJC Scopus subject areas

  • Theoretical Computer Science
  • General Computer Science

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