Machine learning (ML) is increasingly used in clinical oncology to diagnose cancers, predict
patient outcomes, and inform treatment planning. Here, we review recent applications of ML …

This review gives an overview of the current state of deep learning research in breast cancer
imaging. Breast imaging plays a major role in detecting breast cancer at an earlier stage, as …

Training machine learning models in a meaningful order, from the easy samples to the hard
ones, using curriculum learning can provide performance improvements over the standard …

Convolutional neural networks, are one of the most representative deep learning models.
CNNs were extensively used in many aspects of medical image analysis, allowing for great …

Importance Mammography screening currently relies on subjective human interpretation.
Artificial intelligence (AI) advances could be used to increase mammography screening …

Breast cancer remains a global challenge, causing over 600,000 deaths in 2018 (ref.). To
achieve earlier cancer detection, health organizations worldwide recommend screening …

We present a deep convolutional neural network for breast cancer screening exam
classification, trained, and evaluated on over 200000 exams (over 1000000 images). Our …

In the last two decades, Computer Aided Detection (CAD) systems were developed to help
radiologists analyse screening mammograms, however benefits of current CAD …

Improved breast cancer risk models enable targeted screening strategies that achieve
earlier detection and less screening harm than existing guidelines. To bring deep learning …

Screening for breast cancer with mammography has been introduced in various countries
over the last 30 years, initially using analog screen-film-based systems and, over the last 20 …