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| Presentation | 2020-01-29 13:20 [Poster Presentation] Computerized Classification Method of Benign and Malignant Masses in Multiple MRI Sequences using Convolutional Neural Network Yuichi Mima, Akiyoshi Hizukuri, Ryohei Nakayama (Ritsumeikan Univer)  MI2019-77 |
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| Abstract | (in Japanese) | (See Japanese page) |
| (in English) | Breast magnetic resonance imaging (MRI) has a higher sensitivity of early breast cancer than mammography and ultrasonography, but the specificity is lower. The purpose of this study was to develop a computerized classification method for distinguishing between benign and malignant masses by analyzing multiple MRI sequences with convolutional neural networks (CNNs). Our database consisted of multiple MRI sequences for 43 patients with masses. In our proposed method, the CNNs were first trained independently for each MRI sequence. The outputs of the middle layers in the trained CNNs were then inputted to a support vector machine (SVM) for distinguishing between benign and malignant masses. With the proposed method, the classification accuracy, the sensitivity, the specificity, the positive predictive value, and the negative predictive value were 88.4% (38/43), 90.0% (27/30), 84.6% (11/13), 76.9% (10/13), and 93.3% (28/30), respectively. The proposed method achieved high classification performance and would be useful in differential diagnoses of masses as diagnostic aid. | |
| Keyword | (in Japanese) | (See Japanese page) |
| (in English) | Breast magnetic resonance imaging / Multiple sequences, / Mass / Convolutional neural network / / / / | |
| Reference Info. | IEICE Tech. Rep., vol. 119, no. 399, MI2019-77, pp. 57-59, Jan. 2020. | |
| Paper # | MI2019-77 | |
| Date of Issue | 2020-01-22 (MI) | |
| ISSN | Online edition: ISSN 2432-6380 | |
| Copyright and reproduction |
All rights are reserved and no part of this publication may be reproduced or transmitted in any form or by any means, electronic or mechanical, including photocopy, recording, or any information storage and retrieval system, without permission in writing from the publisher. Notwithstanding, instructors are permitted to photocopy isolated articles for noncommercial classroom use without fee. (License No.: 10GA0019/12GB0052/13GB0056/17GB0034/18GB0034) | |
| Download PDF | MI2019-77 |
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| Conference Information | |
| Committee | MI |
| Conference Date | 2020-01-29 - 2020-01-30 |
| Place (in Japanese) | (See Japanese page) |
| Place (in English) | OKINAWAKEN SEINENKAIKAN |
| Topics (in Japanese) | (See Japanese page) |
| Topics (in English) | Medical Image Engineering, Analysis, Recognition, etc. |
| Paper Information | |
| Registration To | MI |
| Conference Code | 2020-01-MI |
| Language | Japanese |
| Title (in Japanese) | (See Japanese page) |
| Sub Title (in Japanese) | (See Japanese page) |
| Title (in English) | Computerized Classification Method of Benign and Malignant Masses in Multiple MRI Sequences using Convolutional Neural Network |
| Sub Title (in English) | |
| Keyword(1) | Breast magnetic resonance imaging |
| Keyword(2) | Multiple sequences, |
| Keyword(3) | Mass |
| Keyword(4) | Convolutional neural network |
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| Keyword(8) | |
| 1st Author's Name | Yuichi Mima |
| 1st Author's Affiliation | Ritsumeikan University (Ritsumeikan Univer) |
| 2nd Author's Name | Akiyoshi Hizukuri |
| 2nd Author's Affiliation | Ritsumeikan University (Ritsumeikan Univer) |
| 3rd Author's Name | Ryohei Nakayama |
| 3rd Author's Affiliation | Ritsumeikan University (Ritsumeikan Univer) |
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| Speaker | Author-1 |
| Date Time | 2020-01-29 13:20:00 |
| Presentation Time | 30 minutes |
| Registration for | MI |
| Paper # | MI2019-77 |
| Volume (vol) | vol.119 |
| Number (no) | no.399 |
| Page | pp.57-59 |
| #Pages | 3 |
| Date of Issue | 2020-01-22 (MI) |
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