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 …

Deep learning analysis of radiological images has the potential to improve diagnostic
accuracy of breast cancer, ultimately leading to better patient outcomes. This paper …

The goal of this study was to employ novel deep-learning convolutional-neural-network
(CNN) to predict pathological complete response (PCR), residual cancer burden (RCB), and …

Deep learning (DL) is one of the most powerful data-driven machine-learning techniques in
artificial intelligence (AI). It can automatically learn from raw data without manual feature …

Medical imaging techniques, such as mammography, ultrasound and magnetic resonance
imaging, plays an integral role in the detection and characterization of breast cancer …

Various MRI sequences have shown their potential to discriminate parotid gland tumors,
including but not limited to T2‐weighted, postcontrast T1‐weighted, and diffusion‐weighted …

Purpose To develop a nomogram based on radiomics signature and deep-learning
signature for predicting the axillary lymph node (ALN) metastasis in breast cancer. Methods …

Breast cancer patients who have pathological complete response (pCR) to neoadjuvant
chemotherapy (NAC) are more likely to have better clinical outcomes. The ability to predict …

This review provides an overview of current applications of deep learning methods within
breast radiology. The diagnostic capabilities of deep learning in breast radiology continue to …

Objective To investigate the ability of our convolutional neural network (CNN) to predict
axillary lymph node metastasis using primary breast cancer ultrasound (US) images …