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Breast Cancer Classification in Automated Breast Ultrasound Using Multiview Convolutional Neural Network with Transfer Learning.
- Source :
-
Ultrasound in medicine & biology [Ultrasound Med Biol] 2020 May; Vol. 46 (5), pp. 1119-1132. Date of Electronic Publication: 2020 Feb 12. - Publication Year :
- 2020
-
Abstract
- To assist radiologists in breast cancer classification in automated breast ultrasound (ABUS) imaging, we propose a computer-aided diagnosis based on a convolutional neural network (CNN) that classifies breast lesions as benign and malignant. The proposed CNN adopts a modified Inception-v3 architecture to provide efficient feature extraction in ABUS imaging. Because the ABUS images can be visualized in transverse and coronal views, the proposed CNN provides an efficient way to extract multiview features from both views. The proposed CNN was trained and evaluated on 316 breast lesions (135 malignant and 181 benign). An observer performance test was conducted to compare five human reviewers' diagnostic performance before and after referring to the predicting outcomes of the proposed CNN. Our method achieved an area under the curve (AUC) value of 0.9468 with five-folder cross-validation, for which the sensitivity and specificity were 0.886 and 0.876, respectively. Compared with conventional machine learning-based feature extraction schemes, particularly principal component analysis (PCA) and histogram of oriented gradients (HOG), our method achieved a significant improvement in classification performance. The proposed CNN achieved a >10% increased AUC value compared with PCA and HOG. During the observer performance test, the diagnostic results of all human reviewers had increased AUC values and sensitivities after referring to the classification results of the proposed CNN, and four of the five human reviewers' AUCs were significantly improved. The proposed CNN employing a multiview strategy showed promise for the diagnosis of breast cancer, and could be used as a second reviewer for increasing diagnostic reliability.<br />Competing Interests: Conflict of interest disclosure The authors declare that they have no conflicts of interest.<br /> (Copyright © 2020 World Federation for Ultrasound in Medicine & Biology. Published by Elsevier Inc. All rights reserved.)
- Subjects :
- Adult
Aged
Area Under Curve
Breast Diseases classification
Breast Diseases diagnostic imaging
Breast Diseases pathology
Breast Neoplasms pathology
Female
Humans
Middle Aged
Principal Component Analysis
Retrospective Studies
Breast Neoplasms classification
Breast Neoplasms diagnostic imaging
Deep Learning
Image Interpretation, Computer-Assisted methods
Neural Networks, Computer
Ultrasonography, Mammary methods
Subjects
Details
- Language :
- English
- ISSN :
- 1879-291X
- Volume :
- 46
- Issue :
- 5
- Database :
- MEDLINE
- Journal :
- Ultrasound in medicine & biology
- Publication Type :
- Academic Journal
- Accession number :
- 32059918
- Full Text :
- https://doi.org/10.1016/j.ultrasmedbio.2020.01.001