{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/cross-modality-fourier-feature-for-medical","title":"Cross-Modality Fourier Feature for Medical Image Synthesis","arxiv_id":null,"date":"2023-07-10","proceeding":"journal 2023 7","authors":["Mei Ma; Ling Lin; Heng Wang; Zhendong Li; Hao Liu"],"abstract":"In this paper, we propose a cross-modality fourier feature (CMFF) method via frequency selection, which learns the rational anatomical structure for targeting medical modality images. Unlike existing works seeking pixel-wise intensity discrepancy likely misleading to bias anatomical structures, our approach strongly holds the medical prior that different modality MRI images should share the same anatomical structure. To achieve this, our method instead learns to convert MRI images to the auxiliary frequency domain. Moreover, we adopt the Shapley value to quantify the contribution of each frequency, with respect to the structure for MRI image pairs of different modalities. Thus our approach learns to refine the anatomical structure generated by the target modality iteratively. Extensive experimental results on the BraTS dataset show that our model surpasses the performance compared to SOTAs.","url_abs":"https://ieeexplore.ieee.org/abstract/document/10219611","url_pdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10219611","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"cross-modality-fourier-feature-for-medical","repo_url":"https://github.com/2023-MindSpore-4/Code12/tree/main/liuhao/Joint%20Statistical%20and%20Causal%20Feature%20Modulated%20Face%20Anti-Spoofing","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":0,"framework":"mindspore","reach":null}],"tasks":[{"task_slug":"image-generation","task_name":"Image Generation"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}