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Data"],"published-print":{"date-parts":[[2024,8,31]]},"abstract":"<jats:p>\n            Currently, the medical field is witnessing an increase in the use of machine learning techniques. Supervised learning methods adopted in classification, prediction, and segmentation tasks for medical images always experience decreased performance when the training and testing datasets do not follow the independent and identically distributed assumption. These distribution shift situations seriously influence machine learning applications\u2019 robustness, fairness, and trustworthiness in the medical domain. Hence, in this article, we adopt the CycleGAN (generative adversarial network) method to cycle train the computed tomography data from different scanners\/manufacturers. It aims to eliminate the distribution shift from diverse data terminals based on our previous work [\n            <jats:xref ref-type=\"bibr\">14<\/jats:xref>\n            ]. However, due to the model collapse problem and generative mechanisms of the GAN-based model, the images we generated contained serious artifacts. To remove the boundary marks and artifacts, we adopt score-based diffusion generative models to refine the images voxel-wisely. This innovative combination of two generative models enhances the quality of data providers while maintaining significant features. Meanwhile, we use five paired patients\u2019 medical images to deal with the evaluation experiments with structural similarity index measure metrics and the segmentation model\u2019s performance comparison. We conclude that CycleGAN can be utilized as an efficient data augmentation technique rather than a distribution-shift-eliminating method. In contrast, the denoising diffusion the denoising diffusion model is more suitable for dealing with the distribution shift problem aroused by the different terminal modules. The limitation of generative methods applied in medical images is the difficulty in obtaining large and diverse datasets that accurately capture the complexity of biological structure and variability. In our following research, we plan to assess the initial and generated datasets to explore more possibilities to overcome the above limitation. We will also incorporate the generative methods into the federated learning architecture, which can maintain their advantages and resolve the distribution shift issue on a larger scale.\n          <\/jats:p>","DOI":"10.1145\/3643034","type":"journal-article","created":{"date-parts":[[2024,1,25]],"date-time":"2024-01-25T12:36:06Z","timestamp":1706186166000},"page":"1-16","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":4,"title":["Voxel-Wise Medical Image Generalization for Eliminating Distribution Shift"],"prefix":"10.1145","volume":"18","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-4815-8547","authenticated-orcid":false,"given":"Feifei","family":"Li","sequence":"first","affiliation":[{"name":"University of Cologne, Faculty of Medicine and University Hospital Cologne, K\u00f6ln, Germany"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1856-5205","authenticated-orcid":false,"given":"Yuanbin","family":"Wang","sequence":"additional","affiliation":[{"name":"University of Cologne, Faculty of Medicine and University Hospital Cologne, K\u00f6ln, Germany"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7611-3501","authenticated-orcid":false,"given":"Oya","family":"Beyan","sequence":"additional","affiliation":[{"name":"University of Cologne, Faculty of Medicine and University Hospital Cologne, K\u00f6ln, Germany and Fraunhofer Institute for Applied Information Technology, FIT, Aachen, Germany"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1630-4012","authenticated-orcid":false,"given":"Mirjam","family":"Sch\u00f6neck","sequence":"additional","affiliation":[{"name":"University of Cologne, Faculty of Medicine and University Hospital Cologne, K\u00f6ln, Germany"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9530-5899","authenticated-orcid":false,"given":"Liliana Lourenco","family":"Caldeira","sequence":"additional","affiliation":[{"name":"University of Cologne, Faculty of Medicine and University Hospital Cologne, K\u00f6ln, Germany"}]}],"member":"320","published-online":{"date-parts":[[2024,6,19]]},"reference":[{"key":"e_1_3_2_2_2","doi-asserted-by":"crossref","unstructured":"Antreas Antoniou Amos Storkey and Harrison Edwards. 2018. 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