Detailed Information

Cited 0 time in webofscience Cited 0 time in scopus
Metadata Downloads

Computer-Aided Diagnosis with Deep Learning Architecture: Applications to Breast Lesions in US Images and Pulmonary Nodules in CT Scans

Authors
Cheng, Jie-ZhiNi, DongChou, Yi-HongQin, JingTiu, Chui-MeiChang, Yeun-ChungHuang, Chiun-ShengShen, DinggangChen, Chung-Ming
Issue Date
15-4월-2016
Publisher
NATURE PUBLISHING GROUP
Citation
SCIENTIFIC REPORTS, v.6
Indexed
SCIE
SCOPUS
Journal Title
SCIENTIFIC REPORTS
Volume
6
URI
https://scholar.korea.ac.kr/handle/2021.sw.korea/88917
DOI
10.1038/srep24454
ISSN
2045-2322
Abstract
This paper performs a comprehensive study on the deep-learning-based computer-aided diagnosis (CADx) for the differential diagnosis of benign and malignant nodules/lesions by avoiding the potential errors caused by inaccurate image processing results (e.g., boundary segmentation), as well as the classification bias resulting from a less robust feature set, as involved in most conventional CADx algorithms. Specifically, the stacked denoising auto-encoder (SDAE) is exploited on the two CADx applications for the differentiation of breast ultrasound lesions and lung CT nodules. The SDAE architecture is well equipped with the automatic feature exploration mechanism and noise tolerance advantage, and hence may be suitable to deal with the intrinsically noisy property of medical image data from various imaging modalities. To show the outperformance of SDAE-based CADx over the conventional scheme, two latest conventional CADx algorithms are implemented for comparison. 10 times of 10-fold cross-validations are conducted to illustrate the efficacy of the SDAE-based CADx algorithm. The experimental results show the significant performance boost by the SDAE-based CADx algorithm over the two conventional methods, suggesting that deep learning techniques can potentially change the design paradigm of the CADx systems without the need of explicit design and selection of problem-oriented features.
Files in This Item
There are no files associated with this item.
Appears in
Collections
Graduate School > Department of Artificial Intelligence > 1. Journal Articles

qrcode

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

Altmetrics

Total Views & Downloads

BROWSE