DeepSinse: deep learning-based detection of single molecules.

Motivation: Imaging single molecules has emerged as a powerful characterization tool in the biological sciences. The detection of these under various noise conditions requires the use of algorithms that are dependent on the end-user inputting several parameters, the choice of which can be challenging and subjective.

Results: In this work, we propose DeepSinse, an easily trainable and useable deep neural network that can detect single molecules with little human input and across a wide range of signal-to-noise ratios.