{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,18]],"date-time":"2026-08-18T15:20:58Z","timestamp":1787066458582,"version":"build-2736575974"},"reference-count":52,"publisher":"SAGE Publications","issue":"3","license":[{"start":{"date-parts":[[2025,12,25]],"date-time":"2025-12-25T00:00:00Z","timestamp":1766620800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/journals.sagepub.com\/page\/policies\/text-and-data-mining-license"}],"funder":[{"DOI":"10.13039\/501100003725","name":"National Research Foundation of Korea","doi-asserted-by":"publisher","award":["NRF-2018R1A5A1025511"],"award-info":[{"award-number":["NRF-2018R1A5A1025511"]}],"id":[{"id":"10.13039\/501100003725","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100003725","name":"National Research Foundation of Korea","doi-asserted-by":"publisher","award":["NRF-RS-2023-00211941"],"award-info":[{"award-number":["NRF-RS-2023-00211941"]}],"id":[{"id":"10.13039\/501100003725","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100003725","name":"National Research Foundation of Korea","doi-asserted-by":"publisher","award":["RS-2025-25420118"],"award-info":[{"award-number":["RS-2025-25420118"]}],"id":[{"id":"10.13039\/501100003725","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["journals.sagepub.com"],"crossmark-restriction":true},"short-container-title":["Ultrason Imaging"],"published-print":{"date-parts":[[2026,5]]},"abstract":"<jats:p>\n                    In medical imaging, segmentation is a critical task for analysis and diagnosis. Deep learning-based segmentation has been actively studied and has shown remarkable performance. Building high-accuracy segmentation models requires a large amount of high-quality labeled data, but the cost of collecting such data is extremely high in medical imaging. In ultrasound imaging, the differences in image features depending on the equipment are significantly greater compared to other medical imaging modalities. Consequently, models need to be trained for each specific device, which entails substantial costs and time, leading to various practical challenges. To address these challenges, we propose a robust and accurate segmentation network that can operate independently of the ultrasound equipment. We integrated the Deep Frequency Filtering (DFF) module into a U-Net-based model. The proposed model retains the U-Net\u2019s encoder-decoder structure but applies frequency filtering within the latent space of each encoder layer, enabling adaptive selection of frequency components for breast tumor detection. Moreover, batch normalization was replaced with instance normalization to remove stylish features. We evaluated the model using three public datasets acquired from different scanners, achieving superior performance on unseen testing datasets compared to existing models. Notably, when tested on the unseen BUS-BRA dataset, DAUS-Net achieved a Dice score of 0.76, compared to 0.61 by the conventional U-Net. This improvement is attributed to the synergy between the DFF module and instance normalization. Our results demonstrate that the proposed model consistently detects and segments breast tumors, highlighting its potential for generalized clinical segmentation task. The source code for implementing DAUS-Net is publicly available at\n                    <jats:ext-link xmlns:xlink=\"http:\/\/www.w3.org\/1999\/xlink\" ext-link-type=\"uri\" xlink:href=\"https:\/\/github.com\/shlee8638\/DAUS-Net\">https:\/\/github.com\/shlee8638\/DAUS-Net<\/jats:ext-link>\n                    .\n                  <\/jats:p>","DOI":"10.1177\/01617346251388454","type":"journal-article","created":{"date-parts":[[2025,12,26]],"date-time":"2025-12-26T06:52:10Z","timestamp":1766731930000},"page":"201-214","update-policy":"https:\/\/doi.org\/10.1177\/sage-journals-update-policy","source":"Crossref","is-referenced-by-count":1,"title":["DAUS-Net: Toward Ultrasound Scanner-Agnostic Domain Generalized Robust and Accurate Segmentation"],"prefix":"10.1177","volume":"48","author":[{"given":"Sangheon","family":"Lee","sequence":"first","affiliation":[{"name":"Department of Robotics and Mechatronics Engineering, DGIST, Daegu, Republic of Korea"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Dongkyu","family":"Jung","sequence":"additional","affiliation":[{"name":"Department of Robotics and Mechatronics Engineering, DGIST, Daegu, Republic of Korea"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Nizar","family":"Guezzi","sequence":"additional","affiliation":[{"name":"Department of Robotics and Mechatronics Engineering, DGIST, Daegu, Republic of Korea"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Sangwoo","family":"Nam","sequence":"additional","affiliation":[{"name":"Department of Robotics and Mechatronics Engineering, DGIST, Daegu, Republic of Korea"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8157-1718","authenticated-orcid":false,"given":"Jaesok","family":"Yu","sequence":"additional","affiliation":[{"name":"Department of Robotics and Mechatronics Engineering, DGIST, Daegu, Republic of Korea"},{"name":"Department of Biomedical Science and Engineering, DGIST, Daegu, Republic of Korea"},{"name":"Department of the Interdisciplinary Studies of Artificial Intelligence, DGIST, Daegu, Republic of Korea"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"179","published-online":{"date-parts":[[2025,12,25]]},"reference":[{"key":"e_1_3_2_2_2","doi-asserted-by":"publisher","DOI":"10.1109\/TMI.2006.877092"},{"key":"e_1_3_2_3_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.ultrasmedbio.2004.11.003"},{"key":"e_1_3_2_4_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.patcog.2018.02.012"},{"key":"e_1_3_2_5_2","doi-asserted-by":"publisher","DOI":"10.1007\/s11548-016-1513-1"},{"key":"e_1_3_2_6_2","doi-asserted-by":"publisher","DOI":"10.1007\/s10549-004-2043-z"},{"key":"e_1_3_2_7_2","doi-asserted-by":"publisher","DOI":"10.1109\/TMI.2002.806427"},{"key":"e_1_3_2_8_2","doi-asserted-by":"publisher","DOI":"10.1148\/radiol.10101414"},{"key":"e_1_3_2_9_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.ultrasmedbio.2020.06.015"},{"key":"e_1_3_2_10_2","doi-asserted-by":"publisher","DOI":"10.1007\/s11548-020-02158-3"},{"key":"e_1_3_2_11_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.bspc.2020.102027"},{"key":"e_1_3_2_12_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.bspc.2021.102721"},{"key":"e_1_3_2_13_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.cmpb.2022.107086"},{"key":"e_1_3_2_14_2","doi-asserted-by":"publisher","DOI":"10.1109\/TMI.2021.3116087"},{"key":"e_1_3_2_15_2","doi-asserted-by":"publisher","DOI":"10.1109\/TMI.2022.3226268"},{"key":"e_1_3_2_16_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.patcog.2023.109728"},{"key":"e_1_3_2_17_2","doi-asserted-by":"publisher","DOI":"10.1109\/ISBI53787.2023.10230609"},{"key":"e_1_3_2_18_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.eswa.2024.123265"},{"key":"e_1_3_2_19_2","volume-title":"Generalize Ultrasound Image Segmentation Via Instant and Plug & Play","author":"Liu Z","year":"2021","unstructured":"Liu Z, Huang X, Yang X, Gao R, Li R, Zhang Y. Generalize Ultrasound Image Segmentation Via Instant and Plug & Play. Shenzhen: Medical UltraSound Image Computing (MUSIC ) Lab, Shenzhen University, China Department of Ultrasound, Luohu People\u2019s Hospital, Shenzhen; 2021. pp.419-23. China School of Biological Sciences and M."},{"key":"e_1_3_2_20_2","doi-asserted-by":"crossref","unstructured":"Kaur B Lema\u02c6 P Mehta R Arbel T. Improving pathological structure segmentation via transfer learning across diseases. In: Wang Q et al. eds. Domain Adaptation and Representation Transfer and Medical Image Learning with Less Labels and Imperfect Data. DART MIL3ID 2019. Lecture Notes in Computer Science vol 11795. Cham: Springer Cham 2019.","DOI":"10.1007\/978-3-030-33391-1_11"},{"key":"e_1_3_2_21_2","doi-asserted-by":"publisher","DOI":"10.1121\/1.5133665"},{"key":"e_1_3_2_22_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.media.2020.101759"},{"issue":"4","key":"e_1_3_2_23_2","article-title":"Domain generalization: A survey","volume":"45","author":"Zhou K","year":"2023","unstructured":"Zhou K, Liu Z, Qiao Y, Xiang T, Loy CC. Domain generalization: A survey. IEEE Trans Pattern Anal Mach Intell. 2023;45(4):4396-415.","journal-title":"IEEE Trans Pattern Anal Mach Intell"},{"key":"e_1_3_2_24_2","doi-asserted-by":"publisher","DOI":"10.1016\/S0009-9260(89)80007-8"},{"key":"e_1_3_2_25_2","doi-asserted-by":"publisher","DOI":"10.1109\/TMI.2014.2377694"},{"key":"e_1_3_2_26_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.compbiomed.2018.04.005"},{"key":"e_1_3_2_27_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.neucom.2021.04.012"},{"key":"e_1_3_2_28_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.media.2023.102904"},{"key":"e_1_3_2_29_2","volume-title":"DiMix: Disentangle-and-Mix Based Domain Generalizable Medical Image Segmentation","author":"Kim H","unstructured":"Kim H, Shin Y, Hwang D. DiMix: Disentangle-and-Mix Based Domain Generalizable Medical Image Segmentation, In Greenspan H, et al., eds. Medical Image Computing and Computer Assisted Intervention \u2013 MICCAI 2023. Lecture Notes in Computer Science, vol 14222. Cham: Springer."},{"key":"e_1_3_2_30_2","doi-asserted-by":"publisher","DOI":"10.1109\/TMM.2021.3104379"},{"key":"e_1_3_2_31_2","doi-asserted-by":"publisher","unstructured":"Pan X Luo P Shi J Tang X. Two at once: enhancing learning and generalization capacities via IBN-Net. Lect. Notes Comput. Sci. (including Subser. Lect. Notes Artif. Intell. Lect. Notes Bioinformatics) vol. 11208 LNCS pp. 484-500 2018 doi:10.1007\/978-3-030-01225-0_29.","DOI":"10.1007\/978-3-030-01225-0_29"},{"key":"e_1_3_2_32_2","doi-asserted-by":"publisher","DOI":"10.1109\/TCYB.2021.3110128"},{"key":"e_1_3_2_33_2","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR46437.2021.00343"},{"key":"e_1_3_2_34_2","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR52729.2023.01135"},{"key":"e_1_3_2_35_2","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR42600.2020.00871"},{"key":"e_1_3_2_36_2","unstructured":"Xu ZJ. Understanding training and generalization in deep learning by Fourier analysis. no. 2002 2018 [Online]. http:\/\/arxiv.org\/abs\/1808.04295"},{"key":"e_1_3_2_37_2","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-319-24574-4"},{"key":"e_1_3_2_38_2","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-01234-2_1"},{"key":"e_1_3_2_39_2","article-title":"Batch normalization: Accelerating deep network training by reducing internal covariate shift","volume":"1","author":"Ioffe S","year":"2015","unstructured":"Ioffe S, Szegedy C. Batch normalization: Accelerating deep network training by reducing internal covariate shift. 32nd Int. Conf. Mach. Learn. ICML 2015. 2015;1:448-56.","journal-title":"32nd Int. Conf. Mach. Learn. ICML 2015"},{"key":"e_1_3_2_40_2","unstructured":"Ulyanov D Vedaldi A Lempitsky V. Instance normalization: The missing ingredient for fast stylization. no. 2016 2016 [Online]. http:\/\/arxiv.org\/abs\/1607.08022"},{"key":"e_1_3_2_41_2","doi-asserted-by":"publisher","DOI":"10.1007\/s10479-005-5724-z"},{"key":"e_1_3_2_42_2","doi-asserted-by":"publisher","DOI":"10.1109\/JBHI.2017.2731873"},{"key":"e_1_3_2_43_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.dib.2019.104863"},{"key":"e_1_3_2_44_2","doi-asserted-by":"publisher","DOI":"10.1002\/mp.16812"},{"key":"e_1_3_2_45_2","doi-asserted-by":"publisher","DOI":"10.1038\/s41597-025-04562-3"},{"key":"e_1_3_2_46_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.compbiomed.2022.106438"},{"issue":"1","key":"e_1_3_2_47_2","first-page":"187","article-title":"Visualizing data using t-SNE","volume":"219","author":"Garc\u00eda-Alonso CR","year":"2014","unstructured":"Garc\u00eda-Alonso CR, P\u00e9rez-Naranjo LM, Fern\u00e1ndez-Caballero JC. Visualizing data using t-SNE. Ann Oper Res. 2014;219(1):187-202.","journal-title":"Ann Oper Res"},{"key":"e_1_3_2_48_2","volume-title":"Advances in Neural Information Processing Systems 32 (NeurIPS 2019)","author":"Paszke A","year":"2019","unstructured":"Paszke A, Gross S, Massa F, Lerer A, Bradbury J, Chanan G, et al. PyTorch: an imperative style, high-performance deep learning library. In: Advances in Neural Information Processing Systems 32 (NeurIPS 2019), Red Hook, NY, USA. Curran Associates Inc., 2019."},{"key":"e_1_3_2_49_2","doi-asserted-by":"publisher","DOI":"10.1186\/s13104-022-06096-y"},{"key":"e_1_3_2_50_2","doi-asserted-by":"publisher","unstructured":"Woolson RF. Wilcoxon Signed-Rank Test. In: D\u2019Agostino RB Sullivan L Massaro J eds. Wiley Encyclopedia of Clinical Trials. doi:10.1002\/9780471462422.eoct979","DOI":"10.1002\/9780471462422.eoct979"},{"issue":"4","key":"e_1_3_2_51_2","article-title":"The control of the false discovery rate in multiple testing under dependency by Yoav Benjamini 1 and Daniel Yekutieli 2","volume":"29","author":"February R","year":"2001","unstructured":"February R. The control of the false discovery rate in multiple testing under dependency by Yoav Benjamini 1 and Daniel Yekutieli 2. Ann Stat. 2001;29(4):1165-88.","journal-title":"Ann Stat"},{"key":"e_1_3_2_52_2","doi-asserted-by":"publisher","DOI":"10.3389\/fpsyg.2013.00863"},{"key":"e_1_3_2_53_2","doi-asserted-by":"publisher","DOI":"10.1109\/TMI.2019.2945514"}],"container-title":["Ultrasonic Imaging"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/journals.sagepub.com\/doi\/pdf\/10.1177\/01617346251388454","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/journals.sagepub.com\/doi\/full-xml\/10.1177\/01617346251388454","content-type":"application\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/journals.sagepub.com\/doi\/pdf\/10.1177\/01617346251388454","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,5,1]],"date-time":"2026-05-01T16:29:36Z","timestamp":1777652976000},"score":1,"resource":{"primary":{"URL":"https:\/\/journals.sagepub.com\/doi\/10.1177\/01617346251388454"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,12,25]]},"references-count":52,"journal-issue":{"issue":"3","published-print":{"date-parts":[[2026,5]]}},"alternative-id":["10.1177\/01617346251388454"],"URL":"https:\/\/doi.org\/10.1177\/01617346251388454","relation":{},"ISSN":["0161-7346","1096-0910"],"issn-type":[{"value":"0161-7346","type":"print"},{"value":"1096-0910","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,12,25]]}}}