Breast cancer is a major health issue and still a leading cause of
fatality among women worldwide. Mammography remains the foremost
effective procedure for the early detection and diagnosis of breast
cancer. The aim of this Special Issue is to present the recent advances
in the detection and diagnosis of cancerous regions in mammograms using
machine learning and deep learning algorithms. We particularly welcome
submissions that will utilize different mammography modalities
(separately or in combination) such as digital mammography (DM),
tomosynthesis, ultrasound or MRI in developing systems to assist the
diagnosis (CADx) and/or the detection (CADe) of regions of suspicion in
mammograms. Submissions can also include but are not limited to novel
feature extraction techniques for breast cancer detection and diagnosis,
transfer learning and deep learning architectures, open access databases
for breast cancer research, generative adversarial network (GAN)
architectures that overcome the problem of small data sets etc.

The intent of this Special Issue is to explore where we stand and what
the future holds in this important health related research topic. To
that end, we invite submissions involving new techniques, methods,
applications, and results, as well as review articles.

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Guest Editors

Prof. Dr. Athanasios Koutras, Electrical & Computer Engineering Dept.,
University of Peloponnese, Greece
Dr. Ioanna Christoyianni, Electrical & Computer Engineering Dept.,
University of Patras, Greece
Dr. George Apostolopoulos, Electrical & Computer Engineering Dept.,
University of Patras, Greece
Prof. Dr. Dermatas Evangelos, Computer Engineering and Informatics
Dept., University of Patras, Greece