Paper Info
Title
Mammogram Denoising To Improve The Calcification Detection Performance Of Convolutional Nets
Abstract
Recently, Convolutional Neural Networks (CNNs) have been successfully used to detect microcalcifications in mammograms. An important step in CNN-based detection is image preprocessing that, in raw mammograms, is usually employed to equalize or remove the intensity-dependent quantum noise. In this work, we show how removing the noise can significantly improve the microcalcification detection performance of a CNN. To this end, we describe the quantum noise with a uniform square-root model. Under this assumption, the generalized Anscombe transformation is applied to the raw mammograms by estimating the noise characteristics from the image at hand. In the Anscombe domain, noise is filtered through an adaptive Wiener filter. The denoised images are recovered with an appropriate inverse transformation and are then used to train the CNN-based detector. Experiments were performed on 1,066 mammograms acquired with GE Senographe systems. MC detection performance of a CNN on noise-free mammograms was statistically significantly higher than on unprocessed mammograms. Results were also superior in comparison with a nonparametric noise-equalizing transformation previously proposed for digital mammograms.
Year
DOI
Venue
2018
10.1117/12.2318069
14TH INTERNATIONAL WORKSHOP ON BREAST IMAGING (IWBI 2018)
Keywords
DocType
Volume
microcalcification detection, denoising, Anscombe transform, convolutional neural networks, deep learning
Conference
10718
ISSN
Citations 
PageRank 
0277-786X
0
0.34
References 
Authors
0
9
Name
Order
Citations
PageRank
Claudio Marrocco18417.53
Alessandro Bria25710.63
Valerio Di Sano300.34
Lucas R. Borges474.26
Benedetta Savelli511.05
Mario Molinara69118.19
Jan-Jurre Mordang7104.30
Nico Karssemeijer8992122.49
Francesco Tortorella937043.39