A small-dataset-trained deep learning framework for identifying atoms on transmission electron microscopy images.

ABSTRACT: To accurately identify atoms on noisy transmission electron microscope images, a deep learning (DL) approach is employed to estimate the map of probabilities at each pixel for being an atom with element discernment. Thanks to a delicately-designed loss function and the ability to extract features, the proposed DL networks can be trained by a small dataset created from approximately 30 experimental images, each with a size of 256 × 256 pixels². The accuracy and robustness of the network were verified by resolving the structural defects of graphene and polar structures in PbTiO₃/SrTiO₃ multilayers from both the general TEM images and their imitated images on which intensities of some pixels lost randomly. Such a network has the potential to identify atoms fro

SUBMITTER: Chen Y 

PROVIDER: S-EPMC9929221 | biostudies-literature | 2023 Feb

REPOSITORIES: biostudies-literature