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• W4320726787 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 from very few images of beam-sensitive material and explosive images recorded in a dynamical atomic process. The idea of using a small-dataset-trained DL framework to resolve a specific problem may prove instructive for practical DL applications in various fields." @default.
• W4320726787 created "2023-02-15" @default.
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• W4320726787 date "2023-02-14" @default.
• W4320726787 modified "2023-09-25" @default.
• W4320726787 title "A small-dataset-trained deep learning framework for identifying atoms on transmission electron microscopy images" @default.
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• W4320726787 doi "https://doi.org/10.1038/s41598-023-29606-9" @default.
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