Breast cancer is the second-most common and leading cause of cancer death among
women. It has become a major health issue in the world over the past 50 years, and its …

The aim of this paper is to review existing approaches to the automatic detection and
segmentation of masses in mammographic images, highlighting the key-points and main …

We present an integrated methodology for detecting, segmenting and classifying breast
masses from mammograms with minimal user intervention. This is a long standing problem …

With recent advances in the field of deep learning, the use of convolutional neural networks
(CNNs) in medical imaging has become very encouraging. The aim of our paper is to …

Mass detection from mammograms plays a crucial role as a pre-processing stage for mass
segmentation and classification. The detection of masses from mammograms is considered …

We describe an automated methodology for the analysis of unregistered cranio-caudal (CC)
and medio-lateral oblique (MLO) mammography views in order to estimate the patient's risk …

Several computer aided diagnosis (CAD) systems have been developed for mammography.
They are widely used in certain countries such as the US where mammography studies are …

Many existing approaches for mammogram analysis are based on single view. Some recent
DNN-based multi-view approaches can perform either bilateral or ipsilateral analysis, while …

The concept of computer-aided detection (CADe) was introduced more than 50 years ago;
however, only in the last 20 years there have been serious and successful attempts at …

Breast cancer presents a substantial health obstacle since it is the most widespread invasive
cancer and the second most common cause of death in women. Prompt identification is …