ArticleScientific reports2020
Partial Scanning Transmission Electron Microscopy with Deep Learning.
Article in Scientific reports, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
9 citing papers in PubMed.
- Recent Advances in Fluorescent Nanoparticles for Stimulated Emission Depletion Imaging.Biosensors · 2024Review
- Zero-shot denoising of microscopy images recorded at high-resolution limits.PLoS computational biology · 2024Article
- Deep learning for automated size and shape analysis of nanoparticles in scanning electron microscopy.RSC advances · 2023Article
- Physics Discovery in Nanoplasmonic Systems via Autonomous Experiments in Scanning Transmission Electron Microscopy.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2022Article
- Three-dimensional-generator U-net for dual-resonant scanning multiphoton microscopy image inpainting and denoising.Biomedical optics express · 2022Article
- Machine Learning Approach for Application-Tailored Nanolubricants' Design.Nanomaterials (Basel, Switzerland) · 2022Article
- Making the Most of your Electrons: Challenges and Opportunities in Characterizing Hybrid Interfaces with STEM.Materials today (Kidlington, England) · 2021Article
- Five-second STEM dislocation tomography for 300 nm thick specimen assisted by deep-learning-based noise filtering.Scientific reports · 2021Article
- Single-atom level determination of 3-dimensional surface atomic structure via neural network-assisted atomic electron tomography.Nature communications · 2021Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
2 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Compressed sensing algorithms are used to decrease electron microscope scan time and electron beam exposure with minimal information loss. Following successful applications of deep learning to compressed sensing, we have developed a two-stage multiscale generative adversarial neural network to complete realistic 512 × 512 scanning transmission electron micrographs from spiral, jittered gridlike, and other partial scans. For spiral scans and mean squared error based pre-training, this enables electron beam coverage to be decreased by 17.9× with a 3.8% test set root mean squared intensity error, and by 87.0× with a 6.2% error. Our generator networks are trained on partial scans created from a new dataset of 16227 scanning transmission electron micrographs. High performance is achieved with adaptive learning rate clipping of loss spikes and an auxiliary trainer network. Our source code, new dataset, and pre-trained models are publicly available.
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.