We have a paper from DGIST AI got accepted to the 25th International Conference on Medical Image Computing and Computer Assisted Intervention, (MICCAI) 2022.
"USG-Net: Deep Learning-based Ultrasound Scanning-Guide for an Orthopedic Sonographer"
Kyungsu Lee, Jaeseung Yang, Moon Hwan Lee, Jin Ho Chang, Jun-Young Kim, and Jae Youn Hwang
Prof. Hwang announced that his recent paper entitled “USG-Net: Deep Learning-based Ultrasound Scanning-Guide for an Orthopedic Sonographer” has been accepted to MICCAI 2022. In the study, he developed a deep learning-based US Scanning-Guide Network (USG-Net) with the automatic dataset construction method based on 3D US images. USG-Net offers a guidance accuracy of 77.86%, demonstrating its potential as a novel tool for assisting sonographers in the diagnosis of various diseases using ultrasound imaging.
"USG-Net: Deep Learning-based Ultrasound Scanning-Guide for an Orthopedic Sonographer"
Kyungsu Lee, Jaeseung Yang, Moon Hwan Lee, Jin Ho Chang, Jun-Young Kim, and Jae Youn Hwang
Prof. Hwang announced that his recent paper entitled “USG-Net: Deep Learning-based Ultrasound Scanning-Guide for an Orthopedic Sonographer” has been accepted to MICCAI 2022. In the study, he developed a deep learning-based US Scanning-Guide Network (USG-Net) with the automatic dataset construction method based on 3D US images. USG-Net offers a guidance accuracy of 77.86%, demonstrating its potential as a novel tool for assisting sonographers in the diagnosis of various diseases using ultrasound imaging.