BACKGROUND: New research fields to design social robots for older people are emerging. By providing support with communication and social interaction, these robots aim to increase quality of life. Because of the decline in functioning due to cognitiv...
In most deep learning-based brain tumor segmentation methods, training the deep network requires annotated tumor areas. However, accurate tumor annotation puts high demands on medical personnel. The aim of this study is to train a deep network for se...
In this work, a dedicated end-to-end deep convolutional neural network, named as Triple-CBCT, is proposed to demonstrate the feasibility of reconstructing three different material distribution volumes from the dual-energy CBCT projection data.In Trip...
The international journal of medical robotics + computer assisted surgery : MRCAS
Jul 8, 2022
INTRODUCTION: Traditional bone surgery using saws and chisels is associated with direct contact of instruments with the bone causing friction, heat and pressure and hence, damaging the bone and the surrounding soft tissues.
Journal of environmental and public health
Jul 7, 2022
With the continuous progress of the economic era, both art and design education and local small and medium-sized enterprises are facing the crisis of survival and the pressure of competition, forcing the two to join hands to resist this crisis. The p...
OBJECTIVE: To evaluate the potential of deep learning models for categorization of dental caries in bitewing radiographs based on the International Caries Classification and Management System (ICCMS™) radiographic scoring system (RSS).
PURPOSE: To assess the clinical feasibility of accelerated deep learning-reconstructed diffusion weighted imaging (DWI) and to compare its image quality and acquisition time with those of conventional DWI.
BACKGROUND: This study aimed to introduce our robotic technique, which can minimize dissection extent using the da Vinci SP robotic system via hairline incision.
PURPOSE: To assess the possibility of reducing the image acquisition time for diffusion-weighted whole-body imaging with background body signal suppression (DWIBS) by denoising with deep learning-based reconstruction (dDLR).
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