{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,27]],"date-time":"2026-08-27T21:58:51Z","timestamp":1787867931857,"version":"build-2784847793"},"reference-count":27,"publisher":"PeerJ","license":[{"start":{"date-parts":[[2024,10,11]],"date-time":"2024-10-11T00:00:00Z","timestamp":1728604800000},"content-version":"unspecified","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"crossref","award":["41902065"],"award-info":[{"award-number":["41902065"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"crossref"}]},{"name":"Third Xinjiang Scientific Expedition Program","award":["2022xjkk1303"],"award-info":[{"award-number":["2022xjkk1303"]}]},{"name":"China Geological Survey Program","award":["ZD20220126"],"award-info":[{"award-number":["ZD20220126"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"abstract":"<jats:p>Breast cancer is a leading cause of death among women worldwide, making early detection and diagnosis critical for effective treatment and improved patient outcomes. Ultrasound imaging is a common diagnostic tool for breast cancer, but interpreting ultrasound images can be challenging due to the complexity of breast tissue and the variability of image quality. This study proposed an Attention Gated Multi ResU-Net model for medical image segmentation tasks, that has shown promising results for breast cancer ultrasound image segmentation. The model\u2019s multi-scale feature extraction and attention-gating mechanism enable it to accurately identify and segment areas of abnormality in the breast tissue, such as masses, cysts, and calcifications. The model\u2019s quantitative test showed an adequate degree of agreement with expert manual annotations, demonstrating its potential for improving early identification and diagnosis of breast cancer. The model\u2019s multi-scale feature extraction and attention-gating mechanism enable it to accurately identify and segment areas of abnormality in the breast tissue, such as masses, cysts, and calcifications, achieving a Dice coefficient of 0.93, sensitivity of 93%, and specificity of 99%. These results underscore the model\u2019s high precision and reliability in medical image analysis.<\/jats:p>","DOI":"10.7717\/peerj-cs.2226","type":"journal-article","created":{"date-parts":[[2024,10,11]],"date-time":"2024-10-11T10:37:24Z","timestamp":1728643044000},"page":"e2226","source":"Crossref","is-referenced-by-count":6,"title":["AI-based automated breast cancer segmentation in ultrasound imaging based on Attention Gated Multi ResU-Net"],"prefix":"10.7717","volume":"10","author":[{"given":"Ting","family":"Ding","sequence":"first","affiliation":[{"name":"School of Earth Science, East China University of Technology, Nanhang, JiangXi, China"},{"name":"Urumqi Comprehensive Survey Center on Natural Resources, Urumq, XinJiang, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Kaimai","family":"Shi","sequence":"additional","affiliation":[{"name":"School of Physics, Georgia Institution of Technology, Atlanta, GA, 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