1 code implementation • 15 Feb 2023 • Ruben T. Lucassen, Mohammad H. Jafari, Nicole M. Duggan, Nick Jowkar, Alireza Mehrtash, Chanel Fischetti, Denie Bernier, Kira Prentice, Erik P. Duhaime, Mike Jin, Purang Abolmaesumi, Friso G. Heslinga, Mitko Veta, Maria A. Duran-Mendicuti, Sarah Frisken, Paul B. Shyn, Alexandra J. Golby, Edward Boyer, William M. Wells, Andrew J. Goldsmith, Tina Kapur
B-line artifacts in LUS videos are key findings associated with pulmonary congestion.
no code implementations • 15 Feb 2021 • Friso G. Heslinga, Ruben T. Lucassen, Myrthe A. van den Berg, Luuk van der Hoek, Josien P. W. Pluim, Javier Cabrerizo, Mark Alberti, Mitko Veta
In this research, deep learning is used to automatically delineate the corneal interfaces and measure corneal thickness with high accuracy in post-DMEK AS-OCT B-scans.
no code implementations • 24 Apr 2020 • Friso G. Heslinga, Mark Alberti, Josien P. W. Pluim, Javier Cabrerizo, Mitko Veta
A second DMEK expert annotated the test set to determine inter-rater performance.
no code implementations • 22 Nov 2019 • Friso G. Heslinga, Josien P. W. Pluim, A. J. H. M. Houben, Miranda T. Schram, Ronald M. A. Henry, Coen D. A. Stehouwer, Marleen J. van Greevenbroek, Tos T. J. M. Berendschot, Mitko Veta
We investigated three methods to achieve high classification performance, measured by the area under the receiver operating curve (ROC-AUC).