no code implementations • 22 Mar 2022 • Morten Rieger Hannemose, Josefine Vilsbøll Sundgaard, Niels Kvorning Ternov, Rasmus R. Paulsen, Anders Nymark Christensen
In this paper, we introduce methods for estimating how hard it is for a doctor to diagnose a case represented by a medical image, both when ground truth difficulties are available for training, and when they are not.
no code implementations • 10 Mar 2022 • Josefine Vilsbøll Sundgaard, Kristine Aavild Juhl, Jakob Mølkjær Slipsager
To enhance performance, cycleGANs were utilized to create a domain-shift between the data sources.
no code implementations • 7 Feb 2022 • Josefine Vilsbøll Sundgaard, Morten Rieger Hannemose, Søren Laugesen, Peter Bray, James Harte, Yosuke Kamide, Chiemi Tanaka, Rasmus R. Paulsen, Anders Nymark Christensen
We present a deep metric variational autoencoder for multi-modal data generation.