Simple Summary Quantitative image analysis of cancers requires accurate tumor
segmentation that is often performed manually. In this study, we developed a deep learning …

Breast magnetic resonance imaging (MRI) plays an important role in high-risk breast cancer
screening, clinical problemsolving, and imaging-based outcome prediction. Breast tumor …

Simple Summary Co-clinical trials are an emerging area of investigation in which a clinical
trial is coupled with a corresponding preclinical trial to inform the corresponding clinical trial …

Breast tumor segmentation based on dynamic contrast-enhanced magnetic resonance
imaging (DCE-MRI) remains an active as well as a challenging problem. Previous studies …

Breast tumor segmentation in medical images is a decisive step for diagnosis and treatment
follow-up. Automating this challenging task helps radiologists to reduce the high manual …

Automatic segmentation of breast cancer lesions in dynamic contrast-enhanced magnetic
resonance imaging is challenged by low accuracy of delineation of the infiltration area …

Breast cancer is a highly prevalent cancer. Triple‐negative breast cancer (TNBC) is more
likely to recur and metastasize than other subtypes of breast cancer. Research on the …

Breast tumor segmentation based on dynamic contrast-enhanced magnetic resonance
imaging (DCE-MRI) is a challenging problem and an active area of research. Particular …

Breast dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) plays an
important role in the screening and treatment evaluation of high-risk breast cancer. The …

Objectives To develop a visual ensemble selection of deep convolutional neural networks
(CNN) for 3D segmentation of breast tumors using T1-weighted dynamic contrast-enhanced …