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Despite outstanding overall performance in segmenting, U-Net still faces from two aspects of challenges: (1) the skip-connections in U-Net have limitations, which may not be able to effectively extract multi-scale features for breast masses with diverse shapes and sizes. (2) U-Net only merges low-level spatial information and high-level semantic information through concatenating, which neglects interdependencies between channels. To address these two problems, we propose the U-shape adaptive scale network (ASU-Net), which contains two modules: adaptive scale module (ASM) and feature refinement module (FRM). In each level of skip-connections, ASM is used to adaptively adjust the receptive fields according to the different scales of the mass, which makes the network adaptively capture multi-scale features. Besides, FRM is employed to allows the decoder to capture channel-wise dependencies, which make the network can selectively emphasize the feature representation of useful channels. Two commonly used mammogram databases including the DDSM-BCRP database and the INbreast database are used to evaluate the segmentation performance of ASU-Net. Finally, ASU-Net obtains the Dice Index (DI) of 91.41% and 93.55% in the DDSM-BCRP database and the INbreast database, respectively.<\/jats:p>","DOI":"10.3233\/jifs-210393","type":"journal-article","created":{"date-parts":[[2021,7,20]],"date-time":"2021-07-20T12:53:43Z","timestamp":1626785623000},"page":"4205-4220","update-policy":"https:\/\/doi.org\/10.1177\/sage-journals-update-policy","source":"Crossref","is-referenced-by-count":7,"title":["ASU-Net: U-shape adaptive scale network for mass segmentation in mammograms"],"prefix":"10.1177","volume":"42","author":[{"given":"Kexin","family":"Sun","sequence":"first","affiliation":[{"name":"Qinghai Normal University","place":["China"]}]},{"given":"Yuelan","family":"Xin","sequence":"additional","affiliation":[{"name":"Qinghai Normal University","place":["China"]}]},{"given":"Yide","family":"Ma","sequence":"additional","affiliation":[{"name":"Lanzhou University","place":["China"]}]},{"given":"Meng","family":"Lou","sequence":"additional","affiliation":[{"name":"Lanzhou University","place":["China"]}]},{"given":"Yunliang","family":"Qi","sequence":"additional","affiliation":[{"name":"Lanzhou University","place":["China"]}]},{"given":"Jie","family":"Zhu","sequence":"additional","affiliation":[{"name":"Qinghai Normal University","place":["China"]}]}],"member":"179","published-online":{"date-parts":[[2021,7,15]]},"reference":[{"key":"e_1_3_2_2_2","doi-asserted-by":"publisher","DOI":"10.1007\/s11548-011-0553-9"},{"key":"e_1_3_2_3_2","doi-asserted-by":"publisher","DOI":"10.12691\/jcrt-3-2-3"},{"key":"e_1_3_2_4_2","doi-asserted-by":"publisher","DOI":"10.1109\/42.974922"},{"key":"e_1_3_2_5_2","doi-asserted-by":"publisher","DOI":"10.1007\/s12609-019-00324-4"},{"key":"e_1_3_2_6_2","doi-asserted-by":"crossref","unstructured":"SzegedyC. 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