Background On mammography many cancers may be missed even in retrospect either due
to the breast density, the small size of the tumor or the subtle signs of cancer that are …

Purpose of review The purpose of this review is to highlight the current role of machine
learning and artificial intelligence and in the field of interventional oncology. Recent findings …

Objectives Use of an AI system based on deep learning to investigate whether the system
can aid in distinguishing malignant from benign calcifications on spot magnification …

Background Computational models based on artificial intelligence (AI) are increasingly used
to diagnose malignant breast lesions. However, assessment from radiologic images of the …

Breast imaging faces challenges with the current increase in medical imaging requests and
lesions that breast screening programs can miss. Solutions to improve these challenges are …

Background A number of publications have demonstrated that deep learning (DL)
algorithms matched or outperformed clinicians in image-based cancer diagnostics, but these …

Purpose In double-reading of screening mammograms, artificial intelligence (AI) algorithms
hold promise as a potential replacement for one of the two readers. The choice of operating …

Purpose To investigate how an artificial intelligence (AI) system performs at digital
mammography (DM) from a screening population with ground truth defined by digital breast …

Purpose In this study, we propose a framework to help the MPE take up a unique and
important role at the introduction of AI solutions in clinical practice, and more in particular at …

Objective This study was conducted in order to investigate the feasibility of using radiomics
analysis (RA) with machine learning algorithms based on breast magnetic resonance (MR) …