Learning Features via Transformer Networks for Cardiomyocyte Profiling.

We introduce an image-based strategy that builds on morphological cell profiling with the purpose of predicting canonical hypertrophy stimulators as proxies for pathomechanisms in cardiology. The traditional approach relies on extracting handcrafted morphological features from unlabeled cell image data in order to reason about cell biology. In this work we employ transformer networks that automatically learn features that help identify which hypertrophy stimulator has been applied on imaged cardiomyocytes. Numerical results illustrate the high predictive performance of this type of neural networks.