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| 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 |
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| 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 | |
| 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 | IMQ2019-8 |
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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 |
| Keyword(5) | |
| Keyword(6) | |
| Keyword(7) | |
| Keyword(8) | |
| 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.) |
| Speaker | Author-2 |
| Date Time | 2019-10-04 14:50:00 |
| Presentation Time | 25 minutes |
| Registration for | IMQ |
| Paper # | IMQ2019-8 |
| Volume (vol) | vol.119 |
| Number (no) | no.215 |
| Page | pp.19-26 |
| #Pages | 8 |
| Date of Issue | 2019-09-27 (IMQ) |
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