bibtext: @InProceedings{ Qia_Medical_MICCAI2024,
                   author = { Qiao, Qiang and Wang, Wenyu and Qu, Meixia and Su, Kun and Jiang, Bin and Guo, Qiang },
                   title = { { Medical Image Segmentation via Single-Source Domain Generalization with Random Amplitude Spectrum Synthesis } },
                   booktitle = {Medical Image Computing and Computer Assisted Intervention -- MICCAI 2024},
                   year = {2024},
                   publisher = {Springer Nature Switzerland},
                   volume = { LNCS 15009 },
                   month = {October},
                   pages = { pending },
                }
abstract: The field of medical image segmentation is challenged by domain generalization (DG) due to domain shifts in clinical datasets. The DG challenge is exacerbated by the scarcity of medical data and privacy concerns. Traditional single-source domain generalization (SSDG) methods primarily rely on stacking data augmentation techniques to minimize domain discrepancies. In this paper, we propose Random Amplitude Spectrum Synthesis (RASS) as a training augmentation for medical images. RASS enhances model generalization by simulating distribution changes from a frequency perspective. This strategy introduces variability by applying amplitude-dependent perturbations to ensure broad coverage of potential domain variations. Furthermore, we propose random mask shuffle and reconstruction components, which can enhance the ability of the backbone to process structural information and increase resilience intra- and cross-domain changes. The proposed Random Amplitude Spectrum Synthesis for Single-Source Domain Generalization (RAS^4DG) is validated on 3D fetal brain images and 2D fundus photography, and achieves an improved DG segmentation performance compared to other SSDG models. The source code is available at: https://github.com/qintianjian-lab/RAS4DG.
title: Medical Image Segmentation via Single-Source Domain Generalization with Random Amplitude Spectrum Synthesis
authors: Qiao, Qiang Wang, Wenyu Qu, Meixia Su, Kun Jiang, Bin Guo, Qiang