Cross Domain Low-Dose CT Image Denoising With Semantic Information Alignment

Abstract

Recently, cross domain adaptation has been applied into quite a few image restoration tasks. While promising performance has been achieved, the domain shift problem between the training set (a.k.a., source domain) and the testing set (a.k.a., target domain) in Low-dose Computed Tomography (LDCT) image denoising tasks is typically ignored by most existing methods. This is prone to the degradation of the denoising performance due to large discrepancy of feature distribution in each dataset from various vendors. Therefore, a simple yet effective LDCT denoising approach has been proposed in this paper to alleviate the domain shift between source and target domains through a novel semantic information alignment. Specifically, we first propose an adaptive version of random frequency mask (RFM) to extract the shared semantic information of cross domains. Then, we incorporate the mask into the existing denoiser to construct a semantic-informationguided objective. Experiments on synthetic and real datasets show our proposed method achieves impressive performance.

Sparse Bayesian Deep Learning for Cross Domain Medical Image Reconstruction

“…In the pursuit of achieving both robustness and adaptability, certain hybrid approaches have emerged, aiming to integrate deep networks into traditional optimization algorithms. One such example is the utilization of deep plug-and-play (PNP) methods (Zhang et al 2021;Huang et al 2022), in which pretrained convolutional neural networks (CNN) are integrated as priors within iterative optimization frameworks designed for various image reconstruction tasks. Regrettably, these methods tend to face the drawback of time-consuming inference processes.…”