DIFFUSIONNET: AN EFFICIENT FRAMEWORK TO CLASSIFY SINGLE-MOLECULE IMAGES WITH LATENT ENTROPY MINIMIZATION

Single-molecule tracking is a powerful tool to measure the dynamics of proteins in living cells. Analysis of molecule diffusion aids our understanding of molecular mechanisms and helps us distinguish between freely diffusing cytosolic protein populations and the more slowly moving membrane-associated population. There is no existing technique that distinguishes the diffusion coefficients of single-molecule images without analyzing their trajectories, which extend over multiple sequential camera frames. To this end, we propose a spatial and channel attention-based convolutional neural network (CNN) architecture with latent entropy minimization that efficiently classifies individual single-molecule images by the imaged molecules' diffusion coefficients. We also propose a loss function which minimizes the entropy of the attention maps. Experiments demonstrate that the diffusion coefficients can efficiently identify different types of molecules in simulated and experimental datasets and our method outperforms state-of-the-art models for image classification (e.g., Resnet-18 and Densenet).