Considering the complementarity of mammography and breast MRI, the research of feature
fusion diagnosis based on cross-modality images was explored to improve the accuracy of …

Human identification by means of dental panoramic X-ray images has been achieved due to
end-to-end deep learning using a convolutional neural network. This paper proposes a …

Breast cancer is a deadly disease affecting women around the globe. Mass detection in the
breast tissue at an early stage can lessen the mortality rate occurring due to breast cancer …

复杂场景分割是自动驾驶领域智能感知的重要任务, 对稳定性和高效性都有较高的要求.
由于一般的场景分割方法主要针对可见光图像, 分割效果非常依赖于图像获取时的光线与气候 …

This study aims to provide a paradigm for recognizing retinal disease in fundus
photographs. Exact and modified evaluation of retinal images has been proposed as a …

Breast density is widely used as an initial indicator of developing breast cancer. At present,
current classification methods for mammographic density usually require manual operations …

The status and prospects of signal processing in the field of healthcare. First, some models
of medical imaging and their factors that affect the image quality are intensively investigated …

Alzheimer's disease (AD) is a type of brain disease that affects a person's ability to perform
daily tasks. Modern neuroimaging techniques have made it possible to detect structural and …

In this study, we present an evidential deep learning framework called MV-DEFEAT,
incorporating the strength of Dempster-Shafer evidential theory and subjective logic, for …

Over the past several decades, medical imaging techniques like magnetic resonance
imaging (MRI), computed tomography (CT), and ultrasound have been extensively utilized …