Classification of round lesions in dual-energy FFDM using a convolutional neural network: simulation study

Brian Toner; Andrey Makeev; Marian Qian; Andreu Badal; Stephen J. Glick

doi:10.1117/12.2582301

15 February 2021 Classification of round lesions in dual-energy FFDM using a convolutional neural network: simulation study

Brian Toner, Andrey Makeev, Marian Qian, Andreu Badal, Stephen J. Glick

Author Affiliations +

Proceedings Volume 11595, Medical Imaging 2021: Physics of Medical Imaging; 115952C (2021) https://doi.org/10.1117/12.2582301
Event: SPIE Medical Imaging, 2021, Online Only

Conference Poster

Abstract

The presence of round cystic and solid mass lesions identified at mammogram screenings account for a large number of recalls. These recalls can cause undue patient anxiety and increased healthcare costs. Since cystic masses are nearly always benign, accurate classification of these lesions would be allow a significant reduction in recalls. This classification is very difficult using conventional mammogram screening data, but this study explores the possibility of performing the task on dual-energy full field digital mammography (FFDM) data. Since clinical data of this type is not readily available, realistic simulated data with different sources of variation are used. With this data, a deep convolutional neural network (CNN) was trained and evaluated. It achieved an AUC of 0.980 and 42% specificity at the 99% sensitivity level. These promising results should motivate further development of such imaging systems.

Conference Presentation

Citation Download Citation

Brian Toner, Andrey Makeev, Marian Qian, Andreu Badal, and Stephen J. Glick "Classification of round lesions in dual-energy FFDM using a convolutional neural network: simulation study", Proc. SPIE 11595, Medical Imaging 2021: Physics of Medical Imaging, 115952C (15 February 2021); https://doi.org/10.1117/12.2582301

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