Cited 0 time in webofscience Cited 0 time in scopus

Weakly supervised lesion localization for age-related macular degeneration detection using optical coherence tomography images

Title
Weakly supervised lesion localization for age-related macular degeneration detection using optical coherence tomography images
Authors
Yang, Hyun LimKim, Jong JinKim, Jong HoKang, Yong KooPark, Dong HoPark, Han SangKim, Hong KyunKim, Min-Soo
DGIST Authors
Kim, Min-Soo
Issue Date
2019-04
Citation
PLoS ONE, 14(4)
Type
Article
Article Type
Article
Keyword
CLASSIFICATION
ISSN
1932-6203
Abstract
Age-related macular degeneration (AMD) is the main cause of irreversible blindness among the elderly and require early diagnosis to prevent vision loss, and careful treatment is essential. Optical coherence tomography (OCT), the most commonly used imaging method in the retinal area for the diagnosis of AMD, is usually interpreted by a clinician, and OCT can help diagnose disease on the basis of the relevant diagnostic criteria, but these judgments can be somewhat subjective. We propose an algorithm for the detection of AMD based on a weakly supervised convolutional neural network (CNN) model to support computer-aided diagnosis (CAD) system. Our main contributions are the following three things. (1) We propose a concise CNN model for OCT images, which outperforms the existing large CNN models using VGG16 and GoogLeNet architectures. (2) We propose an algorithm called Expressive Gradients (EG) that extends the existing Integrated Gradients (IG) algorithm so as to exploit not only the input-level attribution map, but also the high-level attribution maps. Due to enriched gradients, EG can highlight suspicious regions for diagnosis of AMD better than the guided-backpropagation method and IG. (3) Our method provides two visualization options: Overlay and top-k bounding boxes, which would be useful for CAD. Through experimental evaluation using 10,100 clinical OCT images from AMD patients, we demonstrate that our EG algorithm outperforms the IG algorithm in terms of localization accuracy and also outperforms the existing object detection methods in terms of class accuracy. © 2019 Yang et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
URI
http://hdl.handle.net/20.500.11750/9779
DOI
10.1371/journal.pone.0215076
Publisher
Public Library of Science
Related Researcher
  • Author Kim, Min-Soo InfoLab
  • Research Interests Big Data Systems; Big Data Mining & Machine Learning; Big Data Bioinformatics; 데이터 마이닝 및 빅데이터 분석; 바이오인포메틱스 및 뉴로인포메틱스; 뇌-기계 인터페이스(BMI)
Files:
Collection:
Department of Information and Communication EngineeringInfoLab1. Journal Articles


qrcode mendeley

Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.

BROWSE