From Denoising Training to Test-Time Adaptation: Enhancing Domain Generalization for Medical Image Segmentation

• URL: http://arxiv.org/abs/2310.20271v2
• Date: Fri, 3 Nov 2023 03:48:43 GMT
• Title: From Denoising Training to Test-Time Adaptation: Enhancing Domain Generalization for Medical Image Segmentation
• Authors: Ruxue Wen, Hangjie Yuan, Dong Ni, Wenbo Xiao, Yaoyao Wu
• Abstract summary: We propose the Denoising Y-Net (DeY-Net), a novel approach incorporating an auxiliary denoising decoder into the basic U-Net architecture. The auxiliary decoder aims to perform denoising training, augmenting the domain-invariant representation that facilitates domain generalization. Building upon denoising training, we propose Denoising Test Time Adaptation (DeTTA) that further: (i) adapts the model to the target domain in a sample-wise manner, and (ii) adapts to the noise-corrupted input.
• Score: 8.36463803956324
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: In medical image segmentation, domain generalization poses a significant challenge due to domain shifts caused by variations in data acquisition devices and other factors. These shifts are particularly pronounced in the most common scenario, which involves only single-source domain data due to privacy concerns. To address this, we draw inspiration from the self-supervised learning paradigm that effectively discourages overfitting to the source domain. We propose the Denoising Y-Net (DeY-Net), a novel approach incorporating an auxiliary denoising decoder into the basic U-Net architecture. The auxiliary decoder aims to perform denoising training, augmenting the domain-invariant representation that facilitates domain generalization. Furthermore, this paradigm provides the potential to utilize unlabeled data. Building upon denoising training, we propose Denoising Test Time Adaptation (DeTTA) that further: (i) adapts the model to the target domain in a sample-wise manner, and (ii) adapts to the noise-corrupted input. Extensive experiments conducted on widely-adopted liver segmentation benchmarks demonstrate significant domain generalization improvements over our baseline and state-of-the-art results compared to other methods. Code is available at https://github.com/WenRuxue/DeTTA.

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