Ge Precision Healthcare LLC

University of Zürich

Philipp Kaufmann

Sandeep Dutta

Sidharth Abrol

Bipul Das

Amy Deubig

Vanika Singhal

Daphné Gerbaud

Bianca Sintini

Ronny Büchel

Systems/techniques that facilitate learning-based domain transformation for medical images are provided. In various embodiments, a system can access a medical image. In various aspects, the medical image can depict an anatomical structure according to a first medical scanning domain. In various instances, the system can generate, via execution of a machine learning model, a predicted image based on the medical image. In various aspects, the predicted image can depict the anatomical structure according to a second medical scanning domain that is different from the first medical scanning domain. In some cases, the first and second medical scanning domains can be first and second energy levels of a computed tomography (CT) scanning modality. In other cases, the first and second medical scanning domains can be first and second contrast phases of the CT scanning modality.

Learning-based domain transformation for medical images

Growing community of inventors

Zurich, Switzerland

Philipp Kaufmann