Recently, deep learning, especially convolutional neural networks, has achieved the
remarkable results in natural image classification and segmentation. At the same time, in the …

Deep learning methods, especially convolutional neural networks, have been successfully
applied to lesion segmentation in breast ultrasound (BUS) images. However, pattern …

Incorporating human domain knowledge for breast tumor diagnosis is challenging because
shape, boundary, curvature, intensity or other common medical priors vary significantly …

Breast cancer is one of the leading causes of cancer death among women worldwide. In
clinical routine, automatic breast ultrasound (BUS) image segmentation is very challenging …

Breast ultrasound (BUS) image segmentation is challenging and critical for BUS computer-
aided diagnosis (CAD) systems. Many BUS segmentation approaches have been studied in …

Background and objective Breast lesions segmentation is an important step of computer-
aided diagnosis system. However, speckle noise, heterogeneous structure, and similar …

Multistage processing of automated breast ultrasound lesions recognition is dependent on
the performance of prior stages. To improve the current state of the art, we propose the use …

Breast tumor segmentation is an important step in the diagnostic procedure of physicians
and computer-aided diagnosis systems. We propose a two-step deep learning framework for …

Breast cancer is one of the most serious disease affecting women's health. Due to low cost,
portable, no radiation, and high efficiency, breast ultrasound (BUS) imaging is the most …

Breast ultrasound (BUS) image segmentation is challenging and critical for BUS Computer-
Aided Diagnosis (CAD) systems. Many BUS segmentation approaches have been proposed …