{"title":"AWDS-网络：用于描述不同乳腺肿块特征的全场自动分割网络","authors":"Jiajia Jiao, Yingzhao Chen, Zhiyu Li, Tien-Hsiung Weng","doi":"10.1080/09540091.2023.2289836","DOIUrl":null,"url":null,"abstract":"Diverse breast masses in size, shape and place make accurate image segmentation more challenging in a unified deep-learning network. Therefore, based on the U-net network, an adaptive automatic who...","PeriodicalId":50629,"journal":{"name":"Connection Science","volume":"53 1","pages":""},"PeriodicalIF":3.2000,"publicationDate":"2024-01-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"AWDS-net: automatic whole-field segmentation network for characterising diverse breast masses\",\"authors\":\"Jiajia Jiao, Yingzhao Chen, Zhiyu Li, Tien-Hsiung Weng\",\"doi\":\"10.1080/09540091.2023.2289836\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Diverse breast masses in size, shape and place make accurate image segmentation more challenging in a unified deep-learning network. Therefore, based on the U-net network, an adaptive automatic who...\",\"PeriodicalId\":50629,\"journal\":{\"name\":\"Connection Science\",\"volume\":\"53 1\",\"pages\":\"\"},\"PeriodicalIF\":3.2000,\"publicationDate\":\"2024-01-08\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Connection Science\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1080/09540091.2023.2289836\",\"RegionNum\":4,\"RegionCategory\":\"计算机科学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q2\",\"JCRName\":\"COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Connection Science","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1080/09540091.2023.2289836","RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE","Score":null,"Total":0}
AWDS-net: automatic whole-field segmentation network for characterising diverse breast masses
Diverse breast masses in size, shape and place make accurate image segmentation more challenging in a unified deep-learning network. Therefore, based on the U-net network, an adaptive automatic who...
期刊介绍：
Connection Science is an interdisciplinary journal dedicated to exploring the convergence of the analytic and synthetic sciences, including neuroscience, computational modelling, artificial intelligence, machine learning, deep learning, Database, Big Data, quantum computing, Blockchain, Zero-Knowledge, Internet of Things, Cybersecurity, and parallel and distributed computing.
A strong focus is on the articles arising from connectionist, probabilistic, dynamical, or evolutionary approaches in aspects of Computer Science, applied applications, and systems-level computational subjects that seek to understand models in science and engineering.
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