Yuxin Wang

TL;DR: This paper proposes to use convolutional neural networks (CNNs) for segmenting breast ultrasound images into four major tissues: skin, fibroglandular tissue, mass, and fatty tissue, on three‐dimensional (3D) Breast ultrasound images.

TL;DR: In this paper, an adaptive multi-sample-based approach is proposed to enhance the SNR of photoacoustic (PA) signals and in addition, detailed information in rebuilt PA images that used to be buried in the noise can be distinguished.

TL;DR: In this article, a method for extracting and fusing a two-dimensional image from 3D tomography was proposed, which comprises steps that an X-ray tomography apparatus is utilized to emitX-ray signals to the target three-dimensional space, the computer tomography is used to reconstruct a target 3D space image; an ultrasonic sensor is utilized for detecting the target 3d space, and signal acquisition and reconstruction of images in the present detection plane are carried out; a displacement sensor and an angle sensor are utilized to detect the position and angle information of the ultr

TL;DR: An automated algorithm to segment 3D whole breast ultrasound volumes into functionally distinct tissues that may help to correct ultrasound speed of sound aberrations and assist in density based prognosis of breast cancer is proposed.

TL;DR: In this article , the authors evaluated the effect of low radiation dose on CT image quality and lesion diagnostic confidence using a 5-point Likert scale, and compared three levels of DLIR and AISR-V on low-dose radiation abdominal CT images.