{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,31]],"date-time":"2025-10-31T08:06:54Z","timestamp":1761898014868,"version":"3.41.0"},"publisher-location":"New York, NY, USA","reference-count":33,"publisher":"ACM","license":[{"start":{"date-parts":[[2024,11,22]],"date-time":"2024-11-22T00:00:00Z","timestamp":1732233600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.acm.org\/publications\/policies\/copyright_policy#Background"}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":[],"published-print":{"date-parts":[[2024,11,22]]},"DOI":"10.1145\/3708568.3708579","type":"proceedings-article","created":{"date-parts":[[2025,2,27]],"date-time":"2025-02-27T14:55:47Z","timestamp":1740668147000},"page":"69-74","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":1,"title":["SGF-SCA: A Spatial Gated Framework with Shared Channel Attention for Breast Ultrasound Image Segmentation"],"prefix":"10.1145","author":[{"ORCID":"https:\/\/orcid.org\/0009-0005-2341-7508","authenticated-orcid":false,"given":"Tianyu","family":"Xu","sequence":"first","affiliation":[{"name":"School of Computer Science, University of Nottingham Ningbo China, Ningbo, Zhejiang, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6908-3591","authenticated-orcid":false,"given":"Yue","family":"Li","sequence":"additional","affiliation":[{"name":"School of Computer Science, University of Nottingham Ningbo China, Ningbo, Zhejiang, China"}]},{"ORCID":"https:\/\/orcid.org\/0009-0002-1347-5200","authenticated-orcid":false,"given":"Zixiang","family":"Song","sequence":"additional","affiliation":[{"name":"School of Mathematical Sciences, University of Nottingham Ningbo China, Ningbo, Zhejiang, China and School of Mathematical Sciences, University of Nottingham, Nottingham, Nottinghamshire, United Kingdom"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6445-7470","authenticated-orcid":false,"given":"Yipeng","family":"Xu","sequence":"additional","affiliation":[{"name":"School of Computer Science, University of Nottingham Ningbo China, Ningbo, Zhejiang, China"}]},{"ORCID":"https:\/\/orcid.org\/0009-0003-5481-7761","authenticated-orcid":false,"given":"Yuzhe","family":"Wu","sequence":"additional","affiliation":[{"name":"School of Computer Science, University of Nottingham Ningbo China, Ningbo, Zhejiang, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8962-540X","authenticated-orcid":false,"given":"Xiangjian","family":"He","sequence":"additional","affiliation":[{"name":"School of Computer Science, University of Nottingham Ningbo China, Ningbo, Zhejiang, China"}]}],"member":"320","published-online":{"date-parts":[[2025,2,27]]},"reference":[{"key":"e_1_3_3_1_2_2","doi-asserted-by":"crossref","unstructured":"Min Xian Yingtao Zhang Heng-Da Cheng Fei Xu Boyu Zhang and Jianrui Ding. 2018. Automatic breast ultrasound image segmentation: A survey. Pattern Recognition 79 (2018) 340\u2013355.","DOI":"10.1016\/j.patcog.2018.02.012"},{"key":"e_1_3_3_1_3_2","doi-asserted-by":"crossref","unstructured":"Qinghua Huang Yaozhong Luo and Qiangzhi Zhang. 2017. Breast ultrasound image segmentation: a survey. International Journal of Computer Assisted Radiology and Surgery 12 (2017) 493\u2013507.","DOI":"10.1007\/s11548-016-1513-1"},{"key":"e_1_3_3_1_4_2","doi-asserted-by":"crossref","unstructured":"Walid Al-Dhabyani Mohammed Gomaa Hussien Khaled and Aly Fahmy. 2020. Dataset of breast ultrasound images. Data in Brief 28 (2020) 104863.","DOI":"10.1016\/j.dib.2019.104863"},{"key":"e_1_3_3_1_5_2","unstructured":"Jifeng Dai Yi Li Kaiming He and Jian Sun. 2016. R-fcn: Object detection via region-based fully convolutional networks. Advances in Neural Information Processing Systems 29 (2016)."},{"key":"e_1_3_3_1_6_2","doi-asserted-by":"crossref","unstructured":"Liang-Chieh Chen George Papandreou Iasonas Kokkinos Kevin Murphy and Alan\u00a0L Yuille. 2017. Deeplab: Semantic image segmentation with deep convolutional nets atrous convolution and fully connected crfs. IEEE Transactions on Pattern Analysis and Machine Intelligence 40 (2017) 834\u2013848.","DOI":"10.1109\/TPAMI.2017.2699184"},{"key":"e_1_3_3_1_7_2","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-319-24574-4_28"},{"key":"e_1_3_3_1_8_2","doi-asserted-by":"crossref","unstructured":"AN Almustofa A Handayani and TLR Mengko. 2022. Optic disc and optic cup segmentation on retinal image based on multimap localization and u-net convolutional neural network. Journal of Image and Graphics 10 3 (2022) 109\u2013115.","DOI":"10.18178\/joig.10.3.109-115"},{"key":"e_1_3_3_1_9_2","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-00889-5_1"},{"key":"e_1_3_3_1_10_2","unstructured":"Moi\u00a0Hoon Yap Manu Goyal Fatima\u00a0M Osman Robert Mart\u00ed Erika Denton Arne Juette and Reyer Zwiggelaar. 2019. Breast ultrasound lesions recognition: end-to-end deep learning approaches. Journal of Medical Imaging 6 (2019) 011007\u2013011007."},{"key":"e_1_3_3_1_11_2","doi-asserted-by":"crossref","unstructured":"Yuzhou Hu Yi Guo Yuanyuan Wang Jinhua Yu Jiawei Li Shichong Zhou and Cai Chang. 2019. Automatic tumor segmentation in breast ultrasound images using a dilated fully convolutional network combined with an active contour model. Medical physics 46 (2019) 215\u2013228.","DOI":"10.1002\/mp.13268"},{"key":"e_1_3_3_1_12_2","doi-asserted-by":"publisher","DOI":"10.1109\/CALCON49167.2020.9106568"},{"key":"e_1_3_3_1_13_2","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-00764-5_19"},{"key":"e_1_3_3_1_14_2","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-01234-2_49"},{"key":"e_1_3_3_1_15_2","doi-asserted-by":"crossref","unstructured":"Gongping Chen Yu Dai and Jianxun Zhang. 2022. C-Net: Cascaded convolutional neural network with global guidance and refinement residuals for breast ultrasound images segmentation. Computer Methods and Programs in Biomedicine 225 (2022) 107086.","DOI":"10.1016\/j.cmpb.2022.107086"},{"key":"e_1_3_3_1_16_2","unstructured":"Ozan Oktay. 2018. Attention U-Net: Learning where to look for the pancreas. arXiv preprint arXiv:https:\/\/arXiv.org\/abs\/1804.03999 (2018)."},{"key":"e_1_3_3_1_17_2","doi-asserted-by":"crossref","unstructured":"Yuchao Lyu Yinghao Xu Xi Jiang Jianing Liu Xiaoyan Zhao and Xijun Zhu. 2023. AMS-PAN: Breast ultrasound image segmentation model combining attention mechanism and multi-scale features. Biomedical Signal Processing and Control 81 (2023) 104425.","DOI":"10.1016\/j.bspc.2022.104425"},{"key":"e_1_3_3_1_18_2","doi-asserted-by":"publisher","DOI":"10.1109\/ISBI48211.2021.9433899"},{"key":"e_1_3_3_1_19_2","unstructured":"Alexey Dosovitskiy. 2020. An image is worth 16x16 words: Transformers for image recognition at scale. arXiv preprint arXiv:https:\/\/arXiv.org\/abs\/2010.11929 (2020)."},{"key":"e_1_3_3_1_20_2","unstructured":"Jieneng Chen Yongyi Lu Qihang Yu Xiangde Luo Ehsan Adeli Yan Wang Le Lu Alan\u00a0L Yuille and Yuyin Zhou. 2021. Transunet: Transformers make strong encoders for medical image segmentation. arXiv preprint arXiv:https:\/\/arXiv.org\/abs\/2102.04306 (2021)."},{"key":"e_1_3_3_1_21_2","doi-asserted-by":"crossref","unstructured":"Jiaqi Wu Guangxu Li Huimin Lu and Tohru Kamiya. 2021. A supervoxel classification based method for multi-organ segmentation from abdominal ct images. Journal of Image and Graphics 9 1 (2021) 9\u201314.","DOI":"10.18178\/joig.9.1.9-14"},{"key":"e_1_3_3_1_22_2","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV48922.2021.00986"},{"key":"e_1_3_3_1_23_2","volume-title":"European Conference on Computer Vision Workshop","author":"Cao Hu","year":"2022","unstructured":"Hu Cao, Yueyue Wang, Joy Chen, Dongsheng Jiang, Xiaopeng Zhang, Qi Tian, and Manning Wang. 2022. Swin-Unet: Unet-like Pure Transformer for Medical Image Segmentation. In European Conference on Computer Vision Workshop."},{"key":"e_1_3_3_1_24_2","doi-asserted-by":"crossref","unstructured":"Haonan Yang and Dapeng Yang. 2023. CSwin-PNet: A CNN-Swin Transformer combined pyramid network for breast lesion segmentation in ultrasound images. Expert Systems with Applications 213 (2023) 119024.","DOI":"10.1016\/j.eswa.2022.119024"},{"key":"e_1_3_3_1_25_2","unstructured":"Yuzhe Wu Yipeng Xu Tianyu Xu Jialu Zhang Jianfeng Ren and Xudong Jiang. 2024. GCA-SUN: A Gated Context-Aware Swin-UNet for Exemplar-Free Counting. arxiv:https:\/\/arXiv.org\/abs\/2409.12249\u00a0[cs.CV] https:\/\/arxiv.org\/abs\/2409.12249"},{"key":"e_1_3_3_1_26_2","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/D18-1417"},{"key":"e_1_3_3_1_27_2","doi-asserted-by":"crossref","unstructured":"Guizhu Shen Qingping Tan Haoyu Zhang Ping Zeng and Jianjun Xu. 2018. Deep learning with gated recurrent unit networks for financial sequence predictions. Procedia Computer Science 131 (2018) 895\u2013903.","DOI":"10.1016\/j.procs.2018.04.298"},{"key":"e_1_3_3_1_28_2","doi-asserted-by":"crossref","unstructured":"Libo Wang Rui Li Chenxi Duan Ce Zhang Xiaoliang Meng and Shenghui Fang. 2022. A novel transformer based semantic segmentation scheme for fine-resolution remote sensing images. IEEE Geoscience and Remote Sensing Letters 19 (2022) 1\u20135.","DOI":"10.1109\/LGRS.2022.3143368"},{"key":"e_1_3_3_1_29_2","doi-asserted-by":"crossref","unstructured":"Kai Hu Xiang Zhang Dongjin Lee Dapeng Xiong Yuan Zhang and Xieping Gao. 2023. Boundary-guided and region-aware network with global scale-adaptive for accurate segmentation of breast tumors in ultrasound images. IEEE Journal of Biomedical and Health Informatics 27 (2023) 4421\u20134432.","DOI":"10.1109\/JBHI.2023.3285789"},{"key":"e_1_3_3_1_30_2","doi-asserted-by":"crossref","unstructured":"Vijay Badrinarayanan Alex Kendall and Roberto Cipolla. 2017. SegNet: A deep convolutional encoder-decoder architecture for image segmentation. IEEE Transactions on Pattern Analysis and Machine Intelligence 39 (2017) 2481\u20132495.","DOI":"10.1109\/TPAMI.2016.2644615"},{"key":"e_1_3_3_1_31_2","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2017.660"},{"key":"e_1_3_3_1_32_2","first-page":"2262","volume-title":"Healthcare","author":"Shareef Bryar","year":"2022","unstructured":"Bryar Shareef, Aleksandar Vakanski, Phoebe\u00a0E Freer, and Min Xian. 2022. Estan: Enhanced small tumor-aware network for breast ultrasound image segmentation. In Healthcare, Vol.\u00a010. 2262."},{"key":"e_1_3_3_1_33_2","doi-asserted-by":"publisher","DOI":"10.1109\/ISBI52829.2022.9761685"},{"key":"e_1_3_3_1_34_2","doi-asserted-by":"publisher","DOI":"10.1051\/itmconf\/20182300037"}],"event":{"name":"VSIP 2024: 2024 the 6th International Conference on Video, Signal and Image Processing","acronym":"VSIP 2024","location":"Ningbo Hainan China"},"container-title":["Proceedings of the 2024 6th International Conference on Video, Signal and Image Processing"],"original-title":[],"link":[{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3708568.3708579","content-type":"unspecified","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3708568.3708579","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,6,19]],"date-time":"2025-06-19T01:57:03Z","timestamp":1750298223000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3708568.3708579"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,11,22]]},"references-count":33,"alternative-id":["10.1145\/3708568.3708579","10.1145\/3708568"],"URL":"https:\/\/doi.org\/10.1145\/3708568.3708579","relation":{},"subject":[],"published":{"date-parts":[[2024,11,22]]},"assertion":[{"value":"2025-02-27","order":3,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}