Unsupervised content-preserving transformation for optical microscopy.

ABSTRACT: The development of deep learning and open access to a substantial collection of imaging data together provide a potential solution for computational image transformation, which is gradually changing the landscape of optical imaging and biomedical research. However, current implementations of deep learning usually operate in a supervised manner, and their reliance on laborious and error-prone data annotation procedures remains a barrier to more general applicability. Here, we propose an unsupervised image transformation to facilitate the utilization of deep learning for optical microscopy, even in some cases in which supervised models cannot be applied. Through the introduction of a saliency constraint, the unsupervised model, named Unsupervised content-preserving Transformation for Optical

SUBMITTER: Li X 

PROVIDER: S-EPMC7921581 | biostudies-literature | 2021 Mar

REPOSITORIES: biostudies-literature