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Paper Abstract and Keywords
Presentation 2017-11-07 16:00
[Invited Talk] Application of Real-time Image Recognition System with Machine and Transfer Learnings to Computer-Aided Diagnosis for Endoscopic Images of Colorectal Cancer
Tetsushi Koide, Toru Tamaki (Hiroshima Univ.), Shigeto Yoshida, Hiroshi Mieno (Medical Corp. JR Hiroshima Hospital), Shinji Tanaka (Hiroshima Univ. Hospital) CPSY2017-42
Abstract (in Japanese) (See Japanese page) 
(in English) We have developed a real-time computer-aided diagnosis systems for colorectal endoscopic images using machine, transfer, and deep learnings, and it learns clinical doctor’s the NBI magnification findings of colorectal endoscopic images. The developed system can be identified the colorectal endoscopic images either non-neoplastic or neoplastic lesions in real time. We can provide the several software / hardware solutions according to the clinical demands.
The accuracy calculated for evaluating concordance between diagnosis by the system and the histological findings was 94.9% (112/118). Further, the concordance between the endoscopic diagnosis and diagnosis by the system was 97.5% (115/118) (κ=0.94).For the adenomatous histology of lesions (polyps) < 5 mm, the accuracy between the histological findings of diminutive colorectal lesions and the diagnosis by the system was 93.2% (82/88).The concordance between the endoscopic diagnosis and diagnosis by the system was 96,6% (85/88) (κ=0.93), and the agreement was excellent.
Keyword (in Japanese) (See Japanese page) 
(in English) Diagnosis for Colorectal and Gastrointestinal Endoscopic Images / Computer-Aided Diagnosis (CAD) System / Real-Time Image Recognition / Machine Learning / Transfer Learning / Deep Learning / Navigation / Dedicated Hardware  
Reference Info. IEICE Tech. Rep., vol. 117, no. 278, CPSY2017-42, pp. 19-19, Nov. 2017.
Paper # CPSY2017-42 
Date of Issue 2017-10-31 (CPSY) 
ISSN Print edition: ISSN 0913-5685    Online edition: ISSN 2432-6380
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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 VLD DC CPSY RECONF CPM ICD IE IPSJ-SLDM 
Conference Date 2017-11-06 - 2017-11-08 
Place (in Japanese) (See Japanese page) 
Place (in English) Kumamoto-Kenminkouryukan Parea 
Topics (in Japanese) (See Japanese page) 
Topics (in English) Design Gaia 2017 -New Field of VLSI Design- 
Paper Information
Registration To CPSY 
Conference Code 2017-11-VLD-DC-CPSY-RECONF-CPM-ICD-IE-SLDM-EMB-ARC 
Language Japanese 
Title (in Japanese) (See Japanese page) 
Sub Title (in Japanese) (See Japanese page) 
Title (in English) Application of Real-time Image Recognition System with Machine and Transfer Learnings to Computer-Aided Diagnosis for Endoscopic Images of Colorectal Cancer 
Sub Title (in English)  
Keyword(1) Diagnosis for Colorectal and Gastrointestinal Endoscopic Images  
Keyword(2) Computer-Aided Diagnosis (CAD) System  
Keyword(3) Real-Time Image Recognition  
Keyword(4) Machine Learning  
Keyword(5) Transfer Learning  
Keyword(6) Deep Learning  
Keyword(7) Navigation  
Keyword(8) Dedicated Hardware  
1st Author's Name Tetsushi Koide  
1st Author's Affiliation Hiroshima University (Hiroshima Univ.)
2nd Author's Name Toru Tamaki  
2nd Author's Affiliation Hiroshima University (Hiroshima Univ.)
3rd Author's Name Shigeto Yoshida  
3rd Author's Affiliation Medical Corporation JR Hiroshima Hospital (Medical Corp. JR Hiroshima Hospital)
4th Author's Name Hiroshi Mieno  
4th Author's Affiliation Medical Corporation JR Hiroshima Hospital (Medical Corp. JR Hiroshima Hospital)
5th Author's Name Shinji Tanaka  
5th Author's Affiliation Hiroshima University Hospital (Hiroshima Univ. Hospital)
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Speaker Author-1 
Date Time 2017-11-07 16:00:00 
Presentation Time 45 minutes 
Registration for CPSY 
Paper # CPSY2017-42 
Volume (vol) vol.117 
Number (no) no.278 
Page p.19 
#Pages
Date of Issue 2017-10-31 (CPSY) 


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