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Sun K, Xin Y, Ma Y, Lou M, Qi Y, Zhu J. ASU-Net: U-shape adaptive scale network for mass segmentation in mammograms. JOURNAL OF INTELLIGENT & FUZZY SYSTEMS 2022. [DOI: 10.3233/jifs-210393] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/15/2022]
Abstract
U-Net is a commonly used deep learning model for mammogram segmentation. 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.
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Affiliation(s)
- Kexin Sun
- Qinghai Normal University, School of Physics and Electronic Information Engineering, Qinghai Province, Xining, China
| | - Yuelan Xin
- Qinghai Normal University, School of Physics and Electronic Information Engineering, Qinghai Province, Xining, China
| | - Yide Ma
- Lanzhou University, School of Information Science and Engineering, Gansu Province, Lanzhou, China
| | - Meng Lou
- Lanzhou University, School of Information Science and Engineering, Gansu Province, Lanzhou, China
| | - Yunliang Qi
- Lanzhou University, School of Information Science and Engineering, Gansu Province, Lanzhou, China
| | - Jie Zhu
- Qinghai Normal University, School of Physics and Electronic Information Engineering, Qinghai Province, Xining, China
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