ArXiv Preprint
Deep learning (DL) shows notable success in biomedical studies. However, most
DL algorithms work as a black box, exclude biomedical experts, and need
extensive data. We introduce the Self-Enhancing Multi-Photon Artificial
Intelligence (SEMPAI), that integrates hypothesis-driven priors in a
data-driven DL approach for research on multiphoton microscopy (MPM) of muscle
fibers. SEMPAI utilizes meta-learning to optimize prior integration, data
representation, and neural network architecture simultaneously. This allows
hypothesis testing and provides interpretable feedback about the origin of
biological information in MPM images. SEMPAI performs joint learning of several
tasks to enable prediction for small datasets. The method is applied on an
extensive multi-study dataset resulting in the largest joint analysis of
pathologies and function for single muscle fibers. SEMPAI outperforms
state-of-the-art biomarkers in six of seven predictive tasks, including those
with scarce data. SEMPAI's DL models with integrated priors are superior to
those without priors and to prior-only machine learning approaches.
Alexander Mühlberg, Paul Ritter, Simon Langer, Chloë Goossens, Stefanie Nübler, Dominik Schneidereit, Oliver Taubmann, Felix Denzinger, Dominik Nörenberg, Michael Haug, Wolfgang H. Goldmann, Andreas K. Maier, Oliver Friedrich, Lucas Kreiss
2022-10-28
