In recent years, the segmentation of anatomical or pathological structures using deep
learning has experienced a widespread interest in medical image analysis. Remarkably …

Multiparametric magnetic resonance imaging (mpMRI) is an indispensable tool in the
clinical workflow for the diagnosis and treatment planning of various diseases. Machine …

Automatic medical image segmentation plays a critical role in scientific research and
medical care. Existing high-performance deep learning methods typically rely on large …

摘要深度学习能自动从大样本数据中学习获得优良的特征表达, 有效提升各种机器学习任务的
性能, 已广泛应用于信号处理, 计算机视觉和自然语言处理等诸多领域. 基于深度学习的医学影像 …

Breast tumor segmentation of ultrasound images provides valuable information of tumors for
early detection and diagnosis. Accurate segmentation is challenging due to low image …

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 …

Purpose To investigate a novel deep-learning network that synthesizes virtual contrast-
enhanced T1-weighted (vceT1w) magnetic resonance images (MRI) from multimodality …

Purpose We aimed to evaluate deep learning approach with convolutional neural networks
(CNNs) to discriminate between benign and malignant lesions on maximum intensity …

Purpose Breast ultrasound (BUS) image segmentation plays a crucial role in computer‐
aided diagnosis systems for BUS examination, which are useful for improved accuracy of …

Breast tumor segmentation is critical to the diagnosis and treatment of breast cancer. In
clinical breast cancer analysis, experts often examine multi-modal images since such …