{"title":"超声图像模态中的深度学习技术简介","authors":"M. Rai, Priyanka Datta, Reda Ansari","doi":"10.1109/PEEIC47157.2019.8976806","DOIUrl":null,"url":null,"abstract":"Deep learning has recently developed as quickly rising field for the analysis of different medical images. Ultrasound (US) has developed as one of the most frequently clinically used imaging modalities. Although, it is a quickly developing technology but it also has challenges like low imaging quality and high variability. So, it is desirable to progressively develop techniques for automatic analysis of US images for diagnosis. Now a days, Deep learning is also widely used technique for analysis of many US images. In this review, we surveyed different deep learning techniques used for classification, detection, and segmentation along with the challenges of deep learning in US image analysis.","PeriodicalId":203504,"journal":{"name":"2019 2nd International Conference on Power Energy, Environment and Intelligent Control (PEEIC)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2019-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"1","resultStr":"{\"title\":\"An Introduction to Deep Learning Techniques in Ultrasound Image Modality\",\"authors\":\"M. Rai, Priyanka Datta, Reda Ansari\",\"doi\":\"10.1109/PEEIC47157.2019.8976806\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Deep learning has recently developed as quickly rising field for the analysis of different medical images. Ultrasound (US) has developed as one of the most frequently clinically used imaging modalities. Although, it is a quickly developing technology but it also has challenges like low imaging quality and high variability. So, it is desirable to progressively develop techniques for automatic analysis of US images for diagnosis. Now a days, Deep learning is also widely used technique for analysis of many US images. In this review, we surveyed different deep learning techniques used for classification, detection, and segmentation along with the challenges of deep learning in US image analysis.\",\"PeriodicalId\":203504,\"journal\":{\"name\":\"2019 2nd International Conference on Power Energy, Environment and Intelligent Control (PEEIC)\",\"volume\":\"1 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2019-10-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"1\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2019 2nd International Conference on Power Energy, Environment and Intelligent Control (PEEIC)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/PEEIC47157.2019.8976806\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2019 2nd International Conference on Power Energy, Environment and Intelligent Control (PEEIC)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/PEEIC47157.2019.8976806","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
An Introduction to Deep Learning Techniques in Ultrasound Image Modality
Deep learning has recently developed as quickly rising field for the analysis of different medical images. Ultrasound (US) has developed as one of the most frequently clinically used imaging modalities. Although, it is a quickly developing technology but it also has challenges like low imaging quality and high variability. So, it is desirable to progressively develop techniques for automatic analysis of US images for diagnosis. Now a days, Deep learning is also widely used technique for analysis of many US images. In this review, we surveyed different deep learning techniques used for classification, detection, and segmentation along with the challenges of deep learning in US image analysis.