# Quantitative Imaging Principles Improves Medical Image Learning Source: [Quantitative Imaging Principles Improves Medical Image Learning]() Collection index: [llms.txt]() Data updated: 2025-10-27T05:44:10.915Z Authors: Lambert T. Leong, Michael C. Wong, Yannik Glaser, Thomas Wolfgruber, Steven B. Heymsfield, Peter Sadowski, John A. Shepherd Published: 2022-06-14T07:51:49.000Z ## Abstract Fundamental differences between natural and medical images have recently favored the use of self-supervised learning (SSL) over ImageNet transfer learning for medical image applications. Differences between image types are primarily due to the imaging modality and medical images utilize a wide range of physics based techniques while natural images are captured using only visible light. While many have demonstrated that SSL on medical images has resulted in better downstream task performance, our work suggests that more performance can be gained. The scientific principles which are used to acquire medical images are not often considered when constructing learning problems. For this reason, we propose incorporating quantitative imaging principles during generative SSL to improve image quality and quantitative biological accuracy. We show that this training schema results in better starting states for downstream supervised training on limited data. Our model also generates images that validate on clinical quantitative analysis software. ## Recorded benchmark results A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage. ## Original sources and implementation [Original paper]() [PDF]()