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 |
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 |
|