Curriculum-Based Augmented Fourier Domain Adaptation for Robust Medical Image Segmentation

by   An Wang, et al.
The Chinese University of Hong Kong

Accurate and robust medical image segmentation is fundamental and crucial for enhancing the autonomy of computer-aided diagnosis and intervention systems. Medical data collection normally involves different scanners, protocols, and populations, making domain adaptation (DA) a highly demanding research field to alleviate model degradation in the deployment site. To preserve the model performance across multiple testing domains, this work proposes the Curriculum-based Augmented Fourier Domain Adaptation (Curri-AFDA) for robust medical image segmentation. In particular, our curriculum learning strategy is based on the causal relationship of a model under different levels of data shift in the deployment phase, where the higher the shift is, the harder to recognize the variance. Considering this, we progressively introduce more amplitude information from the target domain to the source domain in the frequency space during the curriculum-style training to smoothly schedule the semantic knowledge transfer in an easier-to-harder manner. Besides, we incorporate the training-time chained augmentation mixing to help expand the data distributions while preserving the domain-invariant semantics, which is beneficial for the acquired model to be more robust and generalize better to unseen domains. Extensive experiments on two segmentation tasks of Retina and Nuclei collected from multiple sites and scanners suggest that our proposed method yields superior adaptation and generalization performance. Meanwhile, our approach proves to be more robust under various corruption types and increasing severity levels. In addition, we show our method is also beneficial in the domain-adaptive classification task with skin lesion datasets. The code is available at


page 1

page 2

page 6

page 8

page 9

page 10

page 13


FVP: Fourier Visual Prompting for Source-Free Unsupervised Domain Adaptation of Medical Image Segmentation

Medical image segmentation methods normally perform poorly when there is...

Domain-Adaptive 3D Medical Image Synthesis: An Efficient Unsupervised Approach

Medical image synthesis has attracted increasing attention because it co...

Devil is in Channels: Contrastive Single Domain Generalization for Medical Image Segmentation

Deep learning-based medical image segmentation models suffer from perfor...

e-UDA: Efficient Unsupervised Domain Adaptation for Cross-Site Medical Image Segmentation

Domain adaptation in healthcare data is a potentially critical component...

DomainATM: Domain Adaptation Toolbox for Medical Data Analysis

Domain adaptation (DA) is an important technique for modern machine lear...

AADG: Automatic Augmentation for Domain Generalization on Retinal Image Segmentation

Convolutional neural networks have been widely applied to medical image ...

MDViT: Multi-domain Vision Transformer for Small Medical Image Segmentation Datasets

Despite its clinical utility, medical image segmentation (MIS) remains a...

Please sign up or login with your details

Forgot password? Click here to reset