Classifying symmetrical differences and temporal change for the detection of malignant masses in mammography using deep neural networks

Thijs Kooi; Nico Karssemeijer

doi:10.1117/1.JMI.4.4.044501

10 October 2017 Classifying symmetrical differences and temporal change for the detection of malignant masses in mammography using deep neural networks

Thijs Kooi, Nico Karssemeijer

Author Affiliations +

Journal of Medical Imaging, Vol. 4, Issue 4, 044501 (October 2017). https://doi.org/10.1117/1.JMI.4.4.044501

Abstract

We investigate the addition of symmetry and temporal context information to a deep convolutional neural network (CNN) with the purpose of detecting malignant soft tissue lesions in mammography. We employ a simple linear mapping that takes the location of a mass candidate and maps it to either the contralateral or prior mammogram, and regions of interest (ROIs) are extracted around each location. Two different architectures are subsequently explored: (1) a fusion model employing two datastreams where both ROIs are fed to the network during training and testing and (2) a stagewise approach where a single ROI CNN is trained on the primary image and subsequently used as a feature extractor for both primary and contralateral or prior ROIs. A “shallow” gradient boosted tree classifier is then trained on the concatenation of these features and used to classify the joint representation. The baseline yielded an AUC of 0.87 with confidence interval [0.853, 0.893]. For the analysis of symmetrical differences, the first architecture where both primary and contralateral patches are presented during training obtained an AUC of 0.895 with confidence interval [0.877, 0.913], and the second architecture where a new classifier is retrained on the concatenation an AUC of 0.88 with confidence interval [0.859, 0.9]. We found a significant difference between the first architecture and the baseline at high specificity with p=0.02. When using the same architectures to analyze temporal change, we yielded an AUC of 0.884 with confidence interval [0.865, 0.902] for the first architecture and an AUC of 0.879 with confidence interval [0.858, 0.898] in the second setting. Although improvements for temporal analysis were consistent, they were not found to be significant. The results show our proposed method is promising and we suspect performance can greatly be improved when more temporal data become available.

Citation Download Citation

Thijs Kooi and Nico Karssemeijer "Classifying symmetrical differences and temporal change for the detection of malignant masses in mammography using deep neural networks," Journal of Medical Imaging 4(4), 044501 (10 October 2017). https://doi.org/10.1117/1.JMI.4.4.044501

Received: 21 March 2017; Accepted: 12 September 2017; Published: 10 October 2017

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