A hybrid deep learning approach to predict malignancy of breast lesions using mammograms

Yunzhi Wang; Morteza Heidari; Seyedehnafiseh Mirniaharikandehei; Jing Gong; Wei Qian; Yuchen Qiu; Bin Zheng

doi:10.1117/12.2286555

6 March 2018 A hybrid deep learning approach to predict malignancy of breast lesions using mammograms

Yunzhi Wang, Morteza Heidari, Seyedehnafiseh Mirniaharikandehei, Jing Gong, Wei Qian, Yuchen Qiu, Bin Zheng

Proceedings Volume 10579, Medical Imaging 2018: Imaging Informatics for Healthcare, Research, and Applications; 105790V (2018) https://doi.org/10.1117/12.2286555
Event: SPIE Medical Imaging, 2018, Houston, Texas, United States

Abstract

Applying deep learning technology to medical imaging informatics field has been recently attracting extensive research interest. However, the limited medical image dataset size often reduces performance and robustness of the deep learning based computer-aided detection and/or diagnosis (CAD) schemes. In attempt to address this technical challenge, this study aims to develop and evaluate a new hybrid deep learning based CAD approach to predict likelihood of a breast lesion detected on mammogram being malignant. In this approach, a deep Convolutional Neural Network (CNN) was firstly pre-trained using the ImageNet dataset and serve as a feature extractor. A pseudo-color Region of Interest (ROI) method was used to generate ROIs with RGB channels from the mammographic images as the input to the pre-trained deep network. The transferred CNN features from different layers of the CNN were then obtained and a linear support vector machine (SVM) was trained for the prediction task. By applying to a dataset involving 301 suspicious breast lesions and using a leave-one-case-out validation method, the areas under the ROC curves (AUC) = 0.762 and 0.792 using the traditional CAD scheme and the proposed deep learning based CAD scheme, respectively. An ensemble classifier that combines the classification scores generated by the two schemes yielded an improved AUC value of 0.813. The study results demonstrated feasibility and potentially improved performance of applying a new hybrid deep learning approach to develop CAD scheme using a relatively small dataset of medical images.

Conference Presentation

Citation Download Citation

Yunzhi Wang, Morteza Heidari, Seyedehnafiseh Mirniaharikandehei, Jing Gong, Wei Qian, Yuchen Qiu, and Bin Zheng "A hybrid deep learning approach to predict malignancy of breast lesions using mammograms", Proc. SPIE 10579, Medical Imaging 2018: Imaging Informatics for Healthcare, Research, and Applications, 105790V (6 March 2018); https://doi.org/10.1117/12.2286555

ACCESS THE FULL ARTICLE

INSTITUTIONAL
Select your institution to access the SPIE Digital Library.

SELECT YOUR INSTITUTION

PERSONAL
Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.

PERSONAL SIGN IN

No SPIE Account? Create one

PURCHASE THIS CONTENT

SUBSCRIBE TO DIGITAL LIBRARY

50 downloads per 1-year subscription

Members: $195

Non-members: $335 ADD TO CART

25 downloads per 1 - year subscription

Members: $145

Non-members: $250 ADD TO CART

PURCHASE SINGLE ARTICLE

Includes PDF, HTML & Video, when available