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Paper Abstract and Keywords
Presentation 2019-10-04 14:50
Supporting Colonoscope System for Diagnosis of Depth of Invasion Using Deep Learning and Its Visualization
Nao Ito, Toshiya Nakaguchi (Chiba Univ.), Hiroshi Kawahira (Jichi Medical Univ.), Yuichiro Yoshimura (Chiba Univ.), Hirotaka Nakashima (Kayabacho Clinic), Masaya Uesato, Gaku Ohira, Hideaki Miyauchi, Hisahiro Matsubara (Chiba Univ.) IMQ2019-8
Abstract (in Japanese) (See Japanese page) 
(in English) Colorectal cancer has a high morbidity and is the second most common cancer death in Japan, and is required to be detected and treated at an early stage. When tumorous lesions are pointed out by colonoscopy, if the pathologically benign and malignant cancer, the depth of the wall is important for the subsequent choice of treatment. In the case of cancer, treatment depends on whether the wall depth is Tis (endoscopic mucosal resection), T1a (submucosal resection), or after T1b (surgical resection). Endoscopists need to diagnose the depth of invasion based only on image findings, and the correct diagnosis rate for distinguishing early colorectal cancer from pre-T1a and T1b is reported to be 77.4%. Therefore, in this study, we aim to distinguish normal mucosa, T1a + Tis cancer, and T1b cancer in order to assist the endoscopist in deeply diagnosing advanced colorectal cancer. Although the previous research has proposed a method for diagnosing colorectal polyps by deep learning, support for image recognition has not yet been examined for early-stage cancer depth diagnosis. In this study, we proposed a method of deep penetration diagnosis using deep learning. Learning was performed using Fine-tuning, which used the model structure and weights learned from a large general image data set, and VGG16 was used as the learning model. We used colonoscopy images in normal observation as training images and test images. As a result of evaluation by three-fold cross validation, the effectiveness of the method was shown. In addition, we used Grad-CAM to visualize the point of interest in the input image of the learner in order to efficiently search for the optimal learning setting with the aim of improving the T1b recall. As a result of adjusting the learning parameters using the information obtained from Grad-CAM, the T1b recall was improved.
Keyword (in Japanese) (See Japanese page) 
(in English) Computer-aided Diagnosis / Colorectal cancer / Convolutional neural network / Grad-CAM / / / /  
Reference Info. IEICE Tech. Rep., vol. 119, no. 215, IMQ2019-8, pp. 19-26, Oct. 2019.
Paper # IMQ2019-8 
Date of Issue 2019-09-27 (IMQ) 
ISSN Print edition: ISSN 0913-5685  Online edition: ISSN 2432-6380
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 IMQ  
Conference Date 2019-10-04 - 2019-10-04 
Place (in Japanese) (See Japanese page) 
Place (in English) Osaka University 
Topics (in Japanese) (See Japanese page) 
Topics (in English)  
Paper Information
Registration To IMQ 
Conference Code 2019-10-IMQ 
Language Japanese 
Title (in Japanese) (See Japanese page) 
Sub Title (in Japanese) (See Japanese page) 
Title (in English) Supporting Colonoscope System for Diagnosis of Depth of Invasion Using Deep Learning and Its Visualization 
Sub Title (in English)  
Keyword(1) Computer-aided Diagnosis  
Keyword(2) Colorectal cancer  
Keyword(3) Convolutional neural network  
Keyword(4) Grad-CAM  
1st Author's Name Nao Ito  
1st Author's Affiliation Chiba University (Chiba Univ.)
2nd Author's Name Toshiya Nakaguchi  
2nd Author's Affiliation Chiba University (Chiba Univ.)
3rd Author's Name Hiroshi Kawahira  
3rd Author's Affiliation Jichi Medical University (Jichi Medical Univ.)
4th Author's Name Yuichiro Yoshimura  
4th Author's Affiliation Chiba University (Chiba Univ.)
5th Author's Name Hirotaka Nakashima  
5th Author's Affiliation Kayabacho Clinic (Kayabacho Clinic)
6th Author's Name Masaya Uesato  
6th Author's Affiliation Chiba University (Chiba Univ.)
7th Author's Name Gaku Ohira  
7th Author's Affiliation Chiba University (Chiba Univ.)
8th Author's Name Hideaki Miyauchi  
8th Author's Affiliation Chiba University (Chiba Univ.)
9th Author's Name Hisahiro Matsubara  
9th Author's Affiliation Chiba University (Chiba Univ.)
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Date Time 2019-10-04 14:50:00 
Presentation Time 25 
Registration for IMQ 
Paper # IEICE-IMQ2019-8 
Volume (vol) IEICE-119 
Number (no) no.215 
Page pp.19-26 
#Pages IEICE-8 
Date of Issue IEICE-IMQ-2019-09-27 

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