Comparative Study of Deep Learning Modelsfor Automatic Coronary Stenosis Detectionin X-ray Angiography; CEUR Workshop Proceedings; Vol. 2744 : GraphiCon 2020. Computer Graphics and Machine Vision
| Parent link: | CEUR Workshop Proceedings: Online Proceedings for Scientific Conferences and Workshops Vol. 2744 : GraphiCon 2020. Computer Graphics and Machine Vision.— 2020.— [11 p.] |
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| Collectivité auteur: | |
| Autres auteurs: | , , , , |
| Résumé: | Title screen The article explores the application of machine learning approach to detect both single-vessel and multivessel coronary artery disease from X-ray angiography. Since the interpretation of coronary angiography images requires interventional cardiologists to have considerable training, our study is aimed at analysing, training, and assessing the potential of the existing object detectors for classifying and detecting coronary artery stenosis using angiographic imaging series. 100 patients who underwent coronary angiography at the Research Institute for Complex Issues of Cardiovascular Diseases were retrospectively enrolled in the study. To automate the medical data analysis, we examined and compared three models (SSD MobileNet V1, Faster-RCNN ResNet-50 V1, FasterRCNN NASNet) with various architecture, network complexity, and a number of weights. To compare developed deep learning models, we used the mean Average Precision (mAP) metric, training time, and inference time. Testing results show that the training/inference time is directly proportional to the model complexity. Thus, Faster-RCNN NASNet demonstrates the slowest inference time. Its mean inference time per one image made up 880 ms. In terms of accuracy, FasterRCNN ResNet-50 V1 demonstrates the highest prediction accuracy. This model has reached the mAP metric of 0.92 on the validation dataset. SSD MobileNet V1 has demonstrated the best inference time with the inference rate of 23 frames per second. |
| Langue: | anglais |
| Publié: |
2020
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| Collection: | Session 11: Artificial Intelligence, Cognitive Technology and Robotics |
| Sujets: | |
| Accès en ligne: | http://ceur-ws.org/Vol-2744/paper75.pdf |
| Format: | Électronique Chapitre de livre |
| KOHA link: | https://koha.lib.tpu.ru/cgi-bin/koha/opac-detail.pl?biblionumber=662983 |
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| 200 | 1 | |a Comparative Study of Deep Learning Modelsfor Automatic Coronary Stenosis Detectionin X-ray Angiography |f V. V. Danilov, O. M. Gerget, K. Yu. Klyshnikov[et al.] | |
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| 225 | 1 | |a Session 11: Artificial Intelligence, Cognitive Technology and Robotics | |
| 300 | |a Title screen | ||
| 320 | |a [References: 19 tit.] | ||
| 330 | |a The article explores the application of machine learning approach to detect both single-vessel and multivessel coronary artery disease from X-ray angiography. Since the interpretation of coronary angiography images requires interventional cardiologists to have considerable training, our study is aimed at analysing, training, and assessing the potential of the existing object detectors for classifying and detecting coronary artery stenosis using angiographic imaging series. 100 patients who underwent coronary angiography at the Research Institute for Complex Issues of Cardiovascular Diseases were retrospectively enrolled in the study. To automate the medical data analysis, we examined and compared three models (SSD MobileNet V1, Faster-RCNN ResNet-50 V1, FasterRCNN NASNet) with various architecture, network complexity, and a number of weights. To compare developed deep learning models, we used the mean Average Precision (mAP) metric, training time, and inference time. Testing results show that the training/inference time is directly proportional to the model complexity. Thus, Faster-RCNN NASNet demonstrates the slowest inference time. Its mean inference time per one image made up 880 ms. In terms of accuracy, FasterRCNN ResNet-50 V1 demonstrates the highest prediction accuracy. This model has reached the mAP metric of 0.92 on the validation dataset. SSD MobileNet V1 has demonstrated the best inference time with the inference rate of 23 frames per second. | ||
| 461 | |t CEUR Workshop Proceedings |o Online Proceedings for Scientific Conferences and Workshops | ||
| 463 | |t Vol. 2744 : GraphiCon 2020. Computer Graphics and Machine Vision |o Proceedings of the 30th International Conference, Saint Petersburg, Russia, September 22-25, 2020 |v [11 p.] |d 2020 | ||
| 610 | 1 | |a электронный ресурс | |
| 610 | 1 | |a труды учёных ТПУ | |
| 610 | 1 | |a stenosis detection | |
| 610 | 1 | |a X-ray angiography | |
| 610 | 1 | |a deep learning | |
| 610 | 1 | |a transfer | |
| 610 | 1 | |a learning | |
| 610 | 1 | |a ангиография | |
| 610 | 1 | |a глубокое обучение | |
| 701 | 1 | |a Danilov |b V. V. |c specialist in the field of informatics and computer technology |c engineer of Tomsk Polytechnic University |f 1989- |g Vyacheslav Vladimirovich |3 (RuTPU)RU\TPU\pers\37831 | |
| 701 | 1 | |a Gerget |b O. M. |c Specialist in the field of informatics and computer technology |c Professor of Tomsk Polytechnic University, Doctor of Sciences |f 1974- |g Olga Mikhailovna |3 (RuTPU)RU\TPU\pers\31430 |9 15593 | |
| 701 | 1 | |a Klyshnikov |b K. Yu. |g Kirill Yurjevich | |
| 701 | 1 | |a Ovcharenko |b E. A. |g Evgeny Andreevich | |
| 701 | 1 | |a Frangi |b A. |g Alejandro | |
| 712 | 0 | 2 | |a Национальный исследовательский Томский политехнический университет |b Инженерная школа информационных технологий и робототехники |b Отделение автоматизации и робототехники |3 (RuTPU)RU\TPU\col\23553 |
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