Electron Microscopy
PulseAugur coverage of Electron Microscopy — every cluster mentioning Electron Microscopy across labs, papers, and developer communities, ranked by signal.
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New method aligns AI self-supervised learning with scientific imaging physics
Researchers have developed a new method for designing data augmentations in self-supervised learning (SSL) specifically for scientific imaging. This approach, termed physics-aligned augmentation, considers the unique sy…
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New dual-stream learning enhances electron microscopy imaging
Researchers have developed a novel frequency-aware dual-stream learning architecture to improve electron microscopy imaging. This approach decomposes images into low-frequency structures and high-frequency details, usin…
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AI revolutionizes nanoparticle electron microscopy for scientific inference
A new review paper details the significant advancements of artificial intelligence (AI) in nanoparticle electron microscopy. The paper highlights how AI, particularly machine learning and deep learning techniques, is ev…
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SkelEM framework enhances volume microscopy resolution via signal decoupling
Researchers have developed SkelEM, a novel self-supervised framework for axial super-resolution in volume microscopy. This method decouples the training signals for topological skeleton extraction and diffusion-based de…
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Biologically grounded neural networks leverage mouse brain data
Researchers have developed biologically grounded recurrent neural networks by leveraging data from the MICrONS program, which combines electron microscopy and calcium imaging of mouse visual cortex. These networks utili…
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New methods improve electron microscopy segmentation with sparse labels and preferences
Researchers have developed new methods for domain adaptive segmentation of electron microscopy images, crucial for biological and neuroscience research. The first approach, Instance-Aware Pseudo-Labeling and Class-Focus…