Background Improved breast cancer risk assessment models are needed to enable
personalized screening strategies that achieve better harm-to-benefit ratio based on earlier …

Computer-aided diagnosis (CAD) has been a popular area of research and development in
the past few decades. In CAD, machine learning methods and multidisciplinary knowledge …

Breast density estimation with visual evaluation is still challenging due to low contrast and
significant fluctuations in the mammograms' fatty tissue background. The primary key to …

During the past decade, researchers have investigated the use of computer-aided
mammography interpretation. With the application of deep learning technology, artificial …

Population‐based breast cancer screening using mammography as the gold standard
imaging modality has been in clinical practice for over 40 years. However, the limitations of …

This article aims to build deep learning-based radiomic methods in differentiating vessel
invasion from non-vessel invasion in cervical cancer with multi-parametric MRI data. A set of …

Background Preoperative prediction of epidermal growth factor receptor (EGFR) mutation
status in patients with spinal bone metastases (SBM) from primary lung adenocarcinoma is …

Objectives High breast density is a well-known risk factor for breast cancer. This study aimed
to develop and adapt two (MLO, CC) deep convolutional neural networks (DCNN) for …

This study aimed to assess the diagnostic performance of deep convolutional neural
networks (DCNNs) in classifying breast microcalcification in screening mammograms. To …

Marked enhancement of the fibroglandular tissue on contrast-enhanced breast magnetic
resonance imaging (MRI) may affect lesion detection and classification and is suggested to …