ArticleScience advances2025
Self-supervised machine learning framework for high-throughput electron microscopy.
Article in Science advances, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.
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Who cites it
10 citing papers in PubMed.
- Deep-Learning-Based Denoising for Improved Phase Precision in Electron Holography of Electromagnetic Fields in Nanoscale Materials.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026Article
- Atomistic Kinetics of Dislocation-Mediated Grain Growth in Monolayer MoSAdvanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026Article
- Self-supervised image denoising and restoration method for atomic force microscopy.Nature communications · 2026Article
- Accelerating Structure-Property Relationship Discovery with Multimodal Machine Learning and Self-Driving Microscopy.ACS nano · 2026Article
- Direct Imaging Reveals the Atomic Mechanism of Active-Site Formation in Nanoclusters for Hydrogen Production.Advanced materials (Deerfield Beach, Fla.) · 2026Article
- Multimodal deep-learning optimization of chiroptical properties in all-inorganic perovskite-coated TiONature communications · 2026Article
- Direct Visualization and Quantitative Analysis of Silica Nanoparticle Dispersion States in Saline Solution via Cryo-TEM.ACS measurement science au · 2026Article
- Graphene-Based Liquid Cell Designs for In Situ Liquid-Phase Transmission Electron Microscopy: Recent Developments and Perspectives.Small science · 2025Review
- Nanoscale and Element-Specific Lattice Temperature Measurements Using Core-Loss Electron Energy-Loss Spectroscopy.ACS physical chemistry Au · 2025Article
- Advancing atomic electron tomography with neural networks.Applied microscopy · 2025Review
Corrections and comments
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Authors and funding
12 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Transmission electron microscopy (TEM) is a crucial analysis method in materials science and structural biology, as it offers a high spatiotemporal resolution for structural characterization and reveals structure-property relationships and structural dynamics at atomic and molecular levels. Despite technical advancements in EM, the nature of the electron beam makes the EM imaging inherently detrimental to materials even in low-dose applications. We introduce SHINE, the Self-supervised High-throughput Image denoising Neural network for Electron microscopy, accelerating minimally invasive low-dose EM of diverse material systems. SHINE uses only a single raw image dataset with intrinsic noise, which makes it suitable for limited-size datasets and eliminates the need for expensive ground-truth training datasets. We quantitatively demonstrate that SHINE overcomes the information limit in the current high-resolution TEM, in situ liquid phase TEM, time-series scanning TEM, and cryo-TEM, facilitating unambiguous high-throughput structure analysis across a broad spectrum of materials.
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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.