FD-SDG: Frequency Dropout Based Single Source Domain Generalization Framework for Retinal Vessel Segmentation

Boyang Li, Haojin Li, Yule Zhang, Heng Li*, Jiangyu Chen, Fuhai Pan, Jianwen Chen*, Jiang Liu

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

Single-source domain generalization aims to enhance model performance on unseen target domain test sets using only a single source domain dataset, typically by mitigating domain shifts between domains. In retinal vessel segmentation tasks, differences in dataset composition, such as variations in the proportions of different diseases and imaging noise levels, are considered significant sources of domain shift. However, few previous studies have delved into the mechanisms through which this type of domain shift influences model performance. In this study, we hypothesize that disparities in dataset composition could manifest as differences in distribution patterns of frequency domain features, rendering the model susceptible to overfitting specific patterns. Building on this hypothesis, we propose a novel Frequency Dropout based Single Source Domain Generalization (FD-SDG) framework that employs a Frequency Dropout Randomization mechanism to disentangle complex co-adaptive relationships among features from different frequency bands, thereby enhancing the model’s robustness to variable frequency domain noise patterns in the sample space. Additionally, we introduce a Salient Structure Representation Normalization mechanism to align post-perturbation data features in the feature space using invariant anatomical structures. Through comparison experiments and ablation studies conducted on multiple sets of fundus images across-domain experiments, our method achieves state-of-the-art performance, underscoring its high generalizability and robustness.

Original languageEnglish
Title of host publicationAdvanced Intelligent Computing in Bioinformatics - 20th International Conference, ICIC 2024, Proceedings
EditorsDe-Shuang Huang, Qinhu Zhang, Jiayang Guo
PublisherSpringer Science and Business Media Deutschland GmbH
Pages393-404
Number of pages12
ISBN (Print)9789819756889
DOIs
Publication statusPublished - 2024
Externally publishedYes
Event20th International Conference on Intelligent Computing , ICIC 2024 - Tianjin, China
Duration: 5 Aug 20248 Aug 2024

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume14881 LNBI
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference20th International Conference on Intelligent Computing , ICIC 2024
Country/TerritoryChina
CityTianjin
Period5/08/248/08/24

Keywords

  • domain randomization
  • frequency dropout
  • fundus image
  • single-source domain generalization
  • vessel segmentation

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