{"title":"深度学习在医学图像分析中的研究进展。","authors":"S Suganyadevi, V Seethalakshmi, K Balasamy","doi":"10.1007/s13735-021-00218-1","DOIUrl":null,"url":null,"abstract":"<p><p>Ongoing improvements in AI, particularly concerning deep learning techniques, are assisting to identify, classify, and quantify patterns in clinical images. Deep learning is the quickest developing field in artificial intelligence and is effectively utilized lately in numerous areas, including medication. A brief outline is given on studies carried out on the region of application: neuro, brain, retinal, pneumonic, computerized pathology, bosom, heart, breast, bone, stomach, and musculoskeletal. For information exploration, knowledge deployment, and knowledge-based prediction, deep learning networks can be successfully applied to big data. In the field of medical image processing methods and analysis, fundamental information and state-of-the-art approaches with deep learning are presented in this paper. The primary goals of this paper are to present research on medical image processing as well as to define and implement the key guidelines that are identified and addressed.</p>","PeriodicalId":48501,"journal":{"name":"International Journal of Multimedia Information Retrieval","volume":"11 1","pages":"19-38"},"PeriodicalIF":3.6000,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8417661/pdf/","citationCount":"77","resultStr":"{\"title\":\"A review on deep learning in medical image analysis.\",\"authors\":\"S Suganyadevi, V Seethalakshmi, K Balasamy\",\"doi\":\"10.1007/s13735-021-00218-1\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p><p>Ongoing improvements in AI, particularly concerning deep learning techniques, are assisting to identify, classify, and quantify patterns in clinical images. Deep learning is the quickest developing field in artificial intelligence and is effectively utilized lately in numerous areas, including medication. A brief outline is given on studies carried out on the region of application: neuro, brain, retinal, pneumonic, computerized pathology, bosom, heart, breast, bone, stomach, and musculoskeletal. For information exploration, knowledge deployment, and knowledge-based prediction, deep learning networks can be successfully applied to big data. In the field of medical image processing methods and analysis, fundamental information and state-of-the-art approaches with deep learning are presented in this paper. The primary goals of this paper are to present research on medical image processing as well as to define and implement the key guidelines that are identified and addressed.</p>\",\"PeriodicalId\":48501,\"journal\":{\"name\":\"International Journal of Multimedia Information Retrieval\",\"volume\":\"11 1\",\"pages\":\"19-38\"},\"PeriodicalIF\":3.6000,\"publicationDate\":\"2022-01-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8417661/pdf/\",\"citationCount\":\"77\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"International Journal of Multimedia Information Retrieval\",\"FirstCategoryId\":\"94\",\"ListUrlMain\":\"https://doi.org/10.1007/s13735-021-00218-1\",\"RegionNum\":3,\"RegionCategory\":\"计算机科学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"2021/9/4 0:00:00\",\"PubModel\":\"Epub\",\"JCR\":\"Q2\",\"JCRName\":\"COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"International Journal of Multimedia Information Retrieval","FirstCategoryId":"94","ListUrlMain":"https://doi.org/10.1007/s13735-021-00218-1","RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"2021/9/4 0:00:00","PubModel":"Epub","JCR":"Q2","JCRName":"COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE","Score":null,"Total":0}
A review on deep learning in medical image analysis.
Ongoing improvements in AI, particularly concerning deep learning techniques, are assisting to identify, classify, and quantify patterns in clinical images. Deep learning is the quickest developing field in artificial intelligence and is effectively utilized lately in numerous areas, including medication. A brief outline is given on studies carried out on the region of application: neuro, brain, retinal, pneumonic, computerized pathology, bosom, heart, breast, bone, stomach, and musculoskeletal. For information exploration, knowledge deployment, and knowledge-based prediction, deep learning networks can be successfully applied to big data. In the field of medical image processing methods and analysis, fundamental information and state-of-the-art approaches with deep learning are presented in this paper. The primary goals of this paper are to present research on medical image processing as well as to define and implement the key guidelines that are identified and addressed.
期刊介绍:
Aims and Scope
The International Journal of Multimedia Information Retrieval (IJMIR) is a scholarly archival journal publishing original, peer-reviewed research contributions. Its editorial board strives to present the most important research results in areas within the field of multimedia information retrieval. Core areas include exploration, search, and mining in general collections of multimedia consisting of information from the WWW to scientific imaging to personal archives. Comprehensive review and survey papers that offer up new insights, and lay the foundations for further exploratory and experimental work, are also relevant.
Relevant topics include
Image and video retrieval - theory, algorithms, and systems
Social media interaction and retrieval - collaborative filtering, social voting and ranking
Music and audio retrieval - theory, algorithms, and systems
Scientific and Bio-imaging - MRI, X-ray, ultrasound imaging analysis and retrieval
Semantic learning - visual concept detection, object recognition, and tag learning
Exploration of media archives - browsing, experiential computing
Interfaces - multimedia exploration, visualization, query and retrieval
Multimedia mining - life logs, WWW media mining, pervasive media analysis
Interactive search - interactive learning and relevance feedback in multimedia retrieval
Distributed and high performance media search - efficient and very large scale search
Applications - preserving cultural heritage, 3D graphics models, etc.
Editorial Policies:
We aim for a fast decision time (less than 4 months for the initial decision)
There are no page charges in IJMIR.
Papers are published on line in advance of print publication.
Academic, industrial researchers, and practitioners involved with multimedia search, exploration, and mining will find IJMIR to be an essential source for important results in the field.