AADG: Automatic Augmentation for Domain Generalization on Retinal Image Segmentation
Convolutional neural networks have been widely applied to medical image segmentation and have achieved considerable performance. However, the performance may be significantly affected by the domain gap between training data (source domain) and testing data (target domain). To address this issue, we propose a data manipulation based domain generalization method, called Automated Augmentation for Domain Generalization (AADG). Our AADG framework can effectively sample data augmentation policies that generate novel domains and diversify the training set from an appropriate search space. Specifically, we introduce a novel proxy task maximizing the diversity among multiple augmented novel domains as measured by the Sinkhorn distance in a unit sphere space, making automated augmentation tractable. Adversarial training and deep reinforcement learning are employed to efficiently search the objectives. Quantitative and qualitative experiments on 11 publicly-accessible fundus image datasets (four for retinal vessel segmentation, four for optic disc and cup (OD/OC) segmentation and three for retinal lesion segmentation) are comprehensively performed. Two OCTA datasets for retinal vasculature segmentation are further involved to validate cross-modality generalization. Our proposed AADG exhibits state-of-the-art generalization performance and outperforms existing approaches by considerable margins on retinal vessel, OD/OC and lesion segmentation tasks. The learned policies are empirically validated to be model-agnostic and can transfer well to other models. The source code is available at https://github.com/CRazorback/AADG.
First ; Corresponding
|ESI Research Field|
Cited Times [WOS]:1
|Document Type||Journal Article|
|Department||Department of Electrical and Electronic Engineering|
Department of Electronic and Electrical Engineering, Southern University of Science and Technology, Shenzhen, China
|First Author Affilication||Department of Electrical and Electronic Engineering|
|Corresponding Author Affilication||Department of Electrical and Electronic Engineering|
|First Author's First Affilication||Department of Electrical and Electronic Engineering|
Lyu，Junyan,Zhang，Yiqi,Huang，Yijin,et al. AADG: Automatic Augmentation for Domain Generalization on Retinal Image Segmentation[J]. IEEE Transactions on Medical Imaging,2022,41(12):3699-3711.
Lyu，Junyan,Zhang，Yiqi,Huang，Yijin,Lin，Li,Cheng，Pujin,&Tang，Xiaoying.(2022).AADG: Automatic Augmentation for Domain Generalization on Retinal Image Segmentation.IEEE Transactions on Medical Imaging,41(12),3699-3711.
Lyu，Junyan,et al."AADG: Automatic Augmentation for Domain Generalization on Retinal Image Segmentation".IEEE Transactions on Medical Imaging 41.12(2022):3699-3711.
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