Breast Cancer (BC) is the common form of cancer in women. Its diagnosis and screening are
usually performed through different imaging modalities such as mammography, magnetic …

Recent advances and future perspectives of machine learning techniques offer promising
applications in medical imaging. Machine learning has the potential to improve different …

Historically, medical imaging has been a qualitative or semi-quantitative modality. It is
difficult to quantify what can be seen in an image, and to turn it into valuable predictive …

Recent advancements in signal processing (SP) and machine learning, coupled with
electronic medical record kee** in hospitals and the availability of extensive sets of …

Diagnosis of early invasive breast cancer relies on radiology and clinical evaluation,
supplemented by biopsy confirmation. At least three issues burden this approach: a) …

Purpose To compare the diagnostic performance of radiomic analysis (RA) and a
convolutional neural network (CNN) to radiologists for classification of contrast agent …

Purpose To perform a rapid review of the recent literature on radiomics and breast cancer
(BC). Methods A rapid review, a streamlined approach to systematically identify and …

Introduction: In this review we describe recent developments in the field of radiomics along
with current relevant literature linking it to tumor biology. We furthermore explore the …

Background—Napkin-ring sign (NRS) is an independent prognostic imaging marker of
major adverse cardiac events. However, identification of NRS is challenging because of its …

Breast magnetic resonance imaging (MRI) is the most sensitive imaging modality for breast
cancer diagnosis and is widely used clinically. Dynamic contrast-enhanced MRI is the basis …