Information: Join today and make your research activities more affordable! Technical workshop participation fees and annual registration fees are available at member rates.
Notice: [Important] Announcement of Changes to Registration Fee Payment and Manuscript Upload Procedures for IEICE Technical Meetings
IEICE Technical Committee Submission System
Conference Paper's Information		 Online Proceedings
[Sign in]
Tech. Rep. Archives
 Go Top Page Go Previous   [Japanese] / [English] 

Paper Abstract and Keywords
Presentation 2020-01-30 15:20
Detection and Classification of Cervical Intraepithelial Lesions using Deep Learning
Margaret Manalo, Kota Aoki, Yutaka Ueda, Yu Ito, Yasushi Yagi (Osaka Univ.) MI2019-121
Abstract (in Japanese) (See Japanese page) 
(in English) Cervical cancer remains to have high occurrence and mortality rates in less developed regions due to the lack of diagnostic resources for early detection and cure. Colposcopy, which is considered to be the least invasive screening procedure, would still require the examination of a medical professional. This research focuses on the potential use of deep learning for detecting intraepithelial lesions and cancer from colposcopy images. The goal is to localize and classify these areas, as different stages of the disease correspond to varying rates of progression into cancer as well as appropriate medical treatment. A total of 672 colposcopy images were collected and annotated by a medical staff, with classes ranging from cervical intraepithelial neoplasia (CIN) to cancer. A fully convolutional network (FCN) was used to detect the concerned areas as objects by accessing each image as a whole, utilizing the additional context of epithelial location and color contrast from acetowhite lesions relative to the surrounding tissue. Object detection was performed at three scales for lesions of varying sizes, and logistic regression with a classification threshold was used to label the detections.
Keyword (in Japanese) (See Japanese page) 
(in English) colposcopy / cervical cancer / YOLO / object detection / deep learning / / /  
Reference Info. IEICE Tech. Rep., vol. 119, no. 399, MI2019-121, pp. 237-242, Jan. 2020.
Paper # MI2019-121 
Date of Issue 2020-01-22 (MI) 
ISSN 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 MI2019-121

Conference Information
Committee MI  
Conference Date 2020-01-29 - 2020-01-30 
Place (in Japanese) (See Japanese page) 
Place (in English) OKINAWAKEN SEINENKAIKAN 
Topics (in Japanese) (See Japanese page) 
Topics (in English) Medical Image Engineering, Analysis, Recognition, etc. 
Paper Information
Registration To MI 
Conference Code 2020-01-MI 
Language English 
Title (in Japanese) (See Japanese page) 
Sub Title (in Japanese) (See Japanese page) 
Title (in English) Detection and Classification of Cervical Intraepithelial Lesions using Deep Learning 
Sub Title (in English)  
Keyword(1) colposcopy  
Keyword(2) cervical cancer  
Keyword(3) YOLO  
Keyword(4) object detection  
Keyword(5) deep learning  
Keyword(6)  
Keyword(7)  
Keyword(8)  
1st Author's Name Margaret Manalo  
1st Author's Affiliation Osaka University (Osaka Univ.)
2nd Author's Name Kota Aoki  
2nd Author's Affiliation Osaka University (Osaka Univ.)
3rd Author's Name Yutaka Ueda  
3rd Author's Affiliation Osaka University (Osaka Univ.)
4th Author's Name Yu Ito  
4th Author's Affiliation Osaka University (Osaka Univ.)
5th Author's Name Yasushi Yagi  
5th Author's Affiliation Osaka University (Osaka Univ.)
6th Author's Name  
6th Author's Affiliation ()
7th Author's Name  
7th Author's Affiliation ()
8th Author's Name  
8th Author's Affiliation ()
9th Author's Name  
9th Author's Affiliation ()
10th Author's Name  
10th Author's Affiliation ()
11th Author's Name  
11th Author's Affiliation ()
12th Author's Name  
12th Author's Affiliation ()
13th Author's Name  
13th Author's Affiliation ()
14th Author's Name  
14th Author's Affiliation ()
15th Author's Name  
15th Author's Affiliation ()
16th Author's Name  
16th Author's Affiliation ()
17th Author's Name  
17th Author's Affiliation ()
18th Author's Name  
18th Author's Affiliation ()
19th Author's Name  
19th Author's Affiliation ()
20th Author's Name  
20th Author's Affiliation ()
21st Author's Name  
21st Author's Affiliation ()
22nd Author's Name  
22nd Author's Affiliation ()
23rd Author's Name  
23rd Author's Affiliation ()
24th Author's Name  
24th Author's Affiliation ()
25th Author's Name  
25th Author's Affiliation ()
26th Author's Name / /
26th Author's Affiliation ()
()
27th Author's Name / /
27th Author's Affiliation ()
()
28th Author's Name / /
28th Author's Affiliation ()
()
29th Author's Name / /
29th Author's Affiliation ()
()
30th Author's Name / /
30th Author's Affiliation ()
()
31st Author's Name / /
31st Author's Affiliation ()
()
32nd Author's Name / /
32nd Author's Affiliation ()
()
33rd Author's Name / /
33rd Author's Affiliation ()
()
34th Author's Name / /
34th Author's Affiliation ()
()
35th Author's Name / /
35th Author's Affiliation ()
()
36th Author's Name / /
36th Author's Affiliation ()
()
Speaker Author-1 
Date Time 2020-01-30 15:20:00 
Presentation Time 10 minutes 
Registration for MI 
Paper # MI2019-121 
Volume (vol) vol.119 
Number (no) no.399 
Page pp.237-242 
#Pages 6 
Date of Issue 2020-01-22 (MI) 

[Return to Top Page]

[Return to IEICE Web Page]
The Institute of Electronics, Information and Communication Engineers (IEICE), Japan