1 code implementation • 21 Jun 2022 • Jinu Joseph, Mahesh Raveendranatha Panicker, Yale Tung Chen, Kesavadas Chandrasekharan, Vimal Chacko Mondy, Anoop Ayyappan, Jineesh Valakkada, Kiran Vishnu Narayan
The tool, based on the you look only once (YOLO) network, has the capability of providing the quality of images based on the identification of various LUS landmarks, artefacts and manifestations, prediction of severity of lung infection, possibility of active learning based on the feedback from clinicians or on the image quality and a summarization of the significant frames which are having high severity of infection and high image quality for further analysis.
no code implementations • 30 Mar 2022 • Sandeep Kaushik, Mikael Bylund, Cristina Cozzini, Dattesh Shanbhag, Steven F Petit, Jonathan J Wyatt, Marion I Menzel, Carolin Pirkl, Bhairav Mehta, Vikas Chauhan, Kesavadas Chandrasekharan, Joakim Jonsson, Tufve Nyholm, Florian Wiesinger, Bjoern Menze
In this work, we present a method for synthetic CT (sCT) generation from zero-echo-time (ZTE) MRI aimed at structural and quantitative accuracies of the image, with a particular focus on the accurate bone density value prediction.