A deep learning framework for micro-calcification detection in 2D mammography and C-view

Giovanni Trovini; Christian Napoli; Robert Marti; Amaya Martin; Alessandro Bria; Claudio Marrocco; Mario Molinara; Francesco Tortorella; Oliver Diaz

doi:10.1117/12.2318023

6 July 2018 A deep learning framework for micro-calcification detection in 2D mammography and C-view

Giovanni Trovini, Christian Napoli, Robert Marti, Amaya Martin, Alessandro Bria, Claudio Marrocco, Mario Molinara, Francesco Tortorella, Oliver Diaz

Author Affiliations +

Proceedings Volume 10718, 14th International Workshop on Breast Imaging (IWBI 2018); 1071811 (2018) https://doi.org/10.1117/12.2318023
Event: The Fourteenth International Workshop on Breast Imaging, 2018, Atlanta, Georgia, United States

Abstract

The aim of this paper is to propose a deep learning framework for micro-calcification detection in 2D mammography and in 2D synthetic mammography (C-view) from digital breast tomosynthesis (DBT). The dataset analyzed for 2D mammograms is the INbreast dataset that consists of 410 digital images and we used 360 images with annotated micro-calcifications. For the synthetic views in DBT, we used a private dataset of 245 images, where micro-calcifications were validated by an experienced radiologist. The network is trained in a patch-based fashion, where micro-calcifications are considered positive samples, while patches containing other breast tissues are considered negative. For evaluating the entire dataset, a 2-fold cross validation was performed. In addition, a sliding window method was used to classify new patches within an image with those from the trained model. Considering 5,656 positive samples and 18,000,000 of negative samples, results for the 2D mammography, on the entire dataset, showed an area under the curve (AUC) of 0.9998 and a logarithmic partial area under the curve (logPAUC), in the interval (10⁻⁶ , 1), of 0.8252. Results for the C-View, considering 3,420 positive samples and 11,395,939 of negative samples, showed an AUC, on the entire dataset, of 0.9997 and a logPAUC, in the interval (10⁻⁶ , 1), of 0.8178. In this paper, we illustrate the applied methodologies, the network architecture used for training and test, and the results obtained.

Citation Download Citation

Giovanni Trovini, Christian Napoli, Robert Marti, Amaya Martin, Alessandro Bria, Claudio Marrocco, Mario Molinara, Francesco Tortorella, and Oliver Diaz "A deep learning framework for micro-calcification detection in 2D mammography and C-view", Proc. SPIE 10718, 14th International Workshop on Breast Imaging (IWBI 2018), 1071811 (6 July 2018); https://doi.org/10.1117/12.2318023

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