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Comparison of neural network architectures for segmentation of the left ventricle on EchoCG images
This paper compares the quality of segmentation of echocardiographic images of the left ventricle of the heart using 5 architectures and 38 pre-trained encoders. As part of the study, we trained 1140 neural networks. On the test dataset, the accuracy was 93.18% according to the Dice metric, which is...
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creator | Samun, V. S. Sheka, A. S. Chumarnaya, T. V. Solovyova, O. E. |
description | This paper compares the quality of segmentation of echocardiographic images of the left ventricle of the heart using 5 architectures and 38 pre-trained encoders. As part of the study, we trained 1140 neural networks. On the test dataset, the accuracy was 93.18% according to the Dice metric, which is more than our previous result at 92.78%. On cross-validation, the accuracy was 98.79%, which is higher than the previous result of 90.15%. |
doi_str_mv | 10.1063/5.0032165 |
format | conference_proceeding |
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S. ; Sheka, A. S. ; Chumarnaya, T. V. ; Solovyova, O. E.</creator><contributor>Smirnov, Andrey A ; Kashin, Ilya V ; Narkhov, Evgeniy D ; Volkovich, Vladimir A</contributor><creatorcontrib>Samun, V. S. ; Sheka, A. S. ; Chumarnaya, T. V. ; Solovyova, O. E. ; Smirnov, Andrey A ; Kashin, Ilya V ; Narkhov, Evgeniy D ; Volkovich, Vladimir A</creatorcontrib><description>This paper compares the quality of segmentation of echocardiographic images of the left ventricle of the heart using 5 architectures and 38 pre-trained encoders. As part of the study, we trained 1140 neural networks. On the test dataset, the accuracy was 93.18% according to the Dice metric, which is more than our previous result at 92.78%. 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E.</creatorcontrib><title>Comparison of neural network architectures for segmentation of the left ventricle on EchoCG images</title><title>AIP Conference Proceedings</title><description>This paper compares the quality of segmentation of echocardiographic images of the left ventricle of the heart using 5 architectures and 38 pre-trained encoders. As part of the study, we trained 1140 neural networks. On the test dataset, the accuracy was 93.18% according to the Dice metric, which is more than our previous result at 92.78%. 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E.</au><format>book</format><genre>proceeding</genre><ristype>CONF</ristype><atitle>Comparison of neural network architectures for segmentation of the left ventricle on EchoCG images</atitle><btitle>AIP Conference Proceedings</btitle><date>2020-12-09</date><risdate>2020</risdate><volume>2313</volume><issue>1</issue><issn>0094-243X</issn><eissn>1551-7616</eissn><coden>APCPCS</coden><abstract>This paper compares the quality of segmentation of echocardiographic images of the left ventricle of the heart using 5 architectures and 38 pre-trained encoders. As part of the study, we trained 1140 neural networks. On the test dataset, the accuracy was 93.18% according to the Dice metric, which is more than our previous result at 92.78%. On cross-validation, the accuracy was 98.79%, which is higher than the previous result of 90.15%.</abstract><cop>Melville</cop><pub>American Institute of Physics</pub><doi>10.1063/5.0032165</doi><tpages>6</tpages><oa>free_for_read</oa></addata></record> |
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language | eng |
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source | American Institute of Physics:Jisc Collections:Transitional Journals Agreement 2021-23 (Reading list) |
subjects | Coders Computer architecture Image quality Image segmentation Neural networks |
title | Comparison of neural network architectures for segmentation of the left ventricle on EchoCG images |
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