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Paper Abstract and Keywords
Presentation 2019-01-23 14:00
Unsupervised Shadow Detection for Ultrasound Images by Deep Learning
Suguru Yasutomi (FLL), Akira Sakai (FATEC), Masaaki Komatsu (Riken), Ryu Matsuoka, Reina Komatsu, Tatsuya Arakaki, Mayumi Tokunaka (Showa-U), Hidenori Machino, Kazuma Kobayashi (NCC), Ken Asada (Riken), Syuzo Kaneko (NCC), Akihiko Sekizawa (Showa-U), Ryuji Hamamoto (Riken) MI2018-96
Abstract (in Japanese) (See Japanese page) 
(in English) Medical ultrasound is widely used for diagnosing internal organs since it is non-invasive. Shadows are often appear in ultrasound images, and they hinder diagnosing and image processing. Detecting shadows from the images is important problem in such cases. In this paper, we propose a method to detect shadows using deep learning which is learned by unsupervised way. Specifically, we construct an autoencoder that splits input images into shadows and other contents, and combines them to reconstruct the inputs. The autoencoder is learned to split by newly proposed losses that evaluates characteristics of ultrasound images and its shadows. Effectiveness of the proposed method is shown by experiments on images for embryonic heart diagnosis.
Keyword (in Japanese) (See Japanese page) 
(in English) Ultrasound imaging / Shadow detection / Deep learning / Unsupervised learning / / / /  
Reference Info. IEICE Tech. Rep., vol. 118, no. 412, MI2018-96, pp. 151-156, Jan. 2019.
Paper # MI2018-96 
Date of Issue 2019-01-15 (MI) 
ISSN Online edition: ISSN 2432-6380
Copyright
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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)
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Conference Information
Committee MI  
Conference Date 2019-01-22 - 2019-01-23 
Place (in Japanese) (See Japanese page) 
Place (in English)  
Topics (in Japanese) (See Japanese page) 
Topics (in English) Medical Image Engineering, Analysis, Recognition, etc. 
Paper Information
Registration To MI 
Conference Code 2019-01-MI 
Language Japanese 
Title (in Japanese) (See Japanese page) 
Sub Title (in Japanese) (See Japanese page) 
Title (in English) Unsupervised Shadow Detection for Ultrasound Images by Deep Learning 
Sub Title (in English)  
Keyword(1) Ultrasound imaging  
Keyword(2) Shadow detection  
Keyword(3) Deep learning  
Keyword(4) Unsupervised learning  
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Keyword(6)  
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1st Author's Name Suguru Yasutomi  
1st Author's Affiliation Fujitsu Laboratories Ltd., Artificial Intelligence Laboratory (FLL)
2nd Author's Name Akira Sakai  
2nd Author's Affiliation Fujitsu Advanced Technologies, Division of Development Platform Technology (FATEC)
3rd Author's Name Masaaki Komatsu  
3rd Author's Affiliation RIKEN, AIP Center, Cancer Translational Research Team (Riken)
4th Author's Name Ryu Matsuoka  
4th Author's Affiliation Showa University School of Medicine, Department of Obstetrics and Gynecology (Showa-U)
5th Author's Name Reina Komatsu  
5th Author's Affiliation Showa University School of Medicine, Department of Obstetrics and Gynecology (Showa-U)
6th Author's Name Tatsuya Arakaki  
6th Author's Affiliation Showa University School of Medicine, Department of Obstetrics and Gynecology (Showa-U)
7th Author's Name Mayumi Tokunaka  
7th Author's Affiliation Showa University School of Medicine, Department of Obstetrics and Gynecology (Showa-U)
8th Author's Name Hidenori Machino  
8th Author's Affiliation National Cancer Center Research Institute, Division of Molecular Modification and Cancer Biology (NCC)
9th Author's Name Kazuma Kobayashi  
9th Author's Affiliation National Cancer Center Research Institute, Division of Molecular Modification and Cancer Biology (NCC)
10th Author's Name Ken Asada  
10th Author's Affiliation RIKEN, AIP Center, Cancer Translational Research Team (Riken)
11th Author's Name Syuzo Kaneko  
11th Author's Affiliation National Cancer Center Research Institute, Division of Molecular Modification and Cancer Biology (NCC)
12th Author's Name Akihiko Sekizawa  
12th Author's Affiliation Showa University School of Medicine, Department of Obstetrics and Gynecology (Showa-U)
13th Author's Name Ryuji Hamamoto  
13th Author's Affiliation RIKEN, AIP Center, Cancer Translational Research Team (Riken)
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Speaker Author-1 
Date Time 2019-01-23 14:00:00 
Presentation Time 60 minutes 
Registration for MI 
Paper # MI2018-96 
Volume (vol) vol.118 
Number (no) no.412 
Page pp.151-156 
#Pages
Date of Issue 2019-01-15 (MI) 


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