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Abstract #4342

Self-supervised learning for multi-center MRI harmonization without traveling phantoms: application for cervical cancer classification

Xiao Chang1, Xin Cai1, Yibo Dan2, Yang Song2, Qing Lu3, Guang Yang2, and Shengdong Nie1
1the Institute of Medical Imaging Engineering, University of Shanghai for Science and Technology, Shanghai, China, 2the Shanghai Key Laboratory of Magnetic Resonance, Department of Physics, East China Normal University, Shanghai, China, 3the Department of Radiology, Renji Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai, China


We proposed a self-supervised harmonization to achieve the generality and robustness of diagnostic models in multi-center MRI studies. By mapping the style of images from one center to another center, the harmonization without traveling phantoms was formalized as an unpaired image-to-image translation problem between two domains. The proposed method was demonstrated with pelvic MRI images from two different systems against two state-of-the-art deep-leaning (DL) based methods and one conventional method. The proposed method yields superior generality of diagnostic models by largely decreasing the difference in radiomics features and great image fidelity as quantified by mean structure similarity index measure (MSSIM).

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