Molecular imaging with neural training of identification algorithm (neural network localization identification).

ABSTRACT: Superresolution localization microscopy strongly relies on robust identification algorithms for accurate reconstruction of the biological systems it is used to measure. The fields of machine learning and computer vision have provided promising solutions for automated object identification, but usually rely on well represented training sets to learn object features. However, using a static training set can result in the learned identification algorithm making mistakes on data that is not well represented by the training set. Here, we present a method for training an artificial neural network without providing a training set in advance. This method uses the data to be analyzed, and the fitting algorithm to train an artificial neural network tailored to that data set. We show that the same ar

SUBMITTER: Nelson AJ 

PROVIDER: S-EPMC6240359 | biostudies-literature | 2018 Sep

REPOSITORIES: biostudies-literature