Artificial Convolutional Neural Network in Object Detection and Semantic Segmentation for Medical Imaging Analysis
In the era of digital medicine, a vast number of medical images are produced every day. There is a great demand for intelligent equipment for adjuvant diagnosis to assist medical doctors with different disciplines. With the development of artificial intelligence, the algorithms of convolutional neur...
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| Published in: | Frontiers in oncology Vol. 11; p. 638182 |
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| Language: | English |
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09.03.2021
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| ISSN: | 2234-943X, 2234-943X |
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| Abstract | In the era of digital medicine, a vast number of medical images are produced every day. There is a great demand for intelligent equipment for adjuvant diagnosis to assist medical doctors with different disciplines. With the development of artificial intelligence, the algorithms of convolutional neural network (CNN) progressed rapidly. CNN and its extension algorithms play important roles on medical imaging classification, object detection, and semantic segmentation. While medical imaging classification has been widely reported, the object detection and semantic segmentation of imaging are rarely described. In this review article, we introduce the progression of object detection and semantic segmentation in medical imaging study. We also discuss how to accurately define the location and boundary of diseases. |
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| AbstractList | In the era of digital medicine, a vast number of medical images are produced every day. There is a great demand for intelligent equipment for adjuvant diagnosis to assist medical doctors with different disciplines. With the development of artificial intelligence, the algorithms of convolutional neural network (CNN) progressed rapidly. CNN and its extension algorithms play important roles on medical imaging classification, object detection, and semantic segmentation. While medical imaging classification has been widely reported, the object detection and semantic segmentation of imaging are rarely described. In this review article, we introduce the progression of object detection and semantic segmentation in medical imaging study. We also discuss how to accurately define the location and boundary of diseases. In the era of digital medicine, a vast number of medical images are produced every day. There is a great demand for intelligent equipment for adjuvant diagnosis to assist medical doctors with different disciplines. With the development of artificial intelligence, the algorithms of convolutional neural network (CNN) progressed rapidly. CNN and its extension algorithms play important roles on medical imaging classification, object detection, and semantic segmentation. While medical imaging classification has been widely reported, the object detection and semantic segmentation of imaging are rarely described. In this review article, we introduce the progression of object detection and semantic segmentation in medical imaging study. We also discuss how to accurately define the location and boundary of diseases.In the era of digital medicine, a vast number of medical images are produced every day. There is a great demand for intelligent equipment for adjuvant diagnosis to assist medical doctors with different disciplines. With the development of artificial intelligence, the algorithms of convolutional neural network (CNN) progressed rapidly. CNN and its extension algorithms play important roles on medical imaging classification, object detection, and semantic segmentation. While medical imaging classification has been widely reported, the object detection and semantic segmentation of imaging are rarely described. In this review article, we introduce the progression of object detection and semantic segmentation in medical imaging study. We also discuss how to accurately define the location and boundary of diseases. |
| Author | Yang, Ruixin Yu, Yingyan |
| AuthorAffiliation | Department of General Surgery of Ruijin Hospital, Shanghai Institute of Digestive Surgery and Shanghai Key Laboratory for Gastric Neoplasms, Shanghai Jiao Tong University School of Medicine , Shanghai , China |
| AuthorAffiliation_xml | – name: Department of General Surgery of Ruijin Hospital, Shanghai Institute of Digestive Surgery and Shanghai Key Laboratory for Gastric Neoplasms, Shanghai Jiao Tong University School of Medicine , Shanghai , China |
| Author_xml | – sequence: 1 givenname: Ruixin surname: Yang fullname: Yang, Ruixin – sequence: 2 givenname: Yingyan surname: Yu fullname: Yu, Yingyan |
| BackLink | https://www.ncbi.nlm.nih.gov/pubmed/33768000$$D View this record in MEDLINE/PubMed |
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| Copyright | Copyright © 2021 Yang and Yu. Copyright © 2021 Yang and Yu. 2021 Yang and Yu |
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| Keywords | medical images object detection semantic segmentation analysis convolutional neural network |
| Language | English |
| License | Copyright © 2021 Yang and Yu. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms. |
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| Notes | ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 ObjectType-Review-3 content type line 23 This article was submitted to Cancer Imaging and Image-directed Interventions, a section of the journal Frontiers in Oncology Edited by: Youyong Kong, Southeast University, China Reviewed by: Guanzhen Yu, Shanghai University of Traditional Chinese Medicine, China; Weiming Mi, Tsinghua University, China |
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| Title | Artificial Convolutional Neural Network in Object Detection and Semantic Segmentation for Medical Imaging Analysis |
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