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Cryogenic electron tomography is a technique for imaging biological samples in 3D.
Three-dimensional reconstruction of single particles from random and nonrandom tilt series
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U-Net: convolutional networks for biomedical image segmentation
Ronneberger, O., Fischer, P. & Brox, T · 2015
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Adam: a method for stochastic optimization
Kingma, D. P. & Ba, J · 2015
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Cryo-electron tomography and subtomogram averaging
Wan, W. & Briggs, J. A · 2016
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Principles of cryo-EM single-particle image processing
Sigworth, F. J · 2016
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ICON: 3D reconstruction with ‘missing-information’ restoration in biological electron tomography
Deng, Y. et al · 2016
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Noise2Noise: learning image restoration without clean data
Lehtinen, J. et al · 2018
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Deep image prior
Ulyanov, D., Vedaldi, A. & Lempitsky, V · 2018
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Deep decoder: concise image representations from untrained non-convolutional networks
Heckel, R. & Hand, P · 2018
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Cryo-CARE: content-aware image restoration for cryo-transmission electron microscopy data
Buchholz, T.-O., Jordan, M., Pigino, G. & Jug, F · 2019
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Real-time cryo-electron microscopy data preprocessing with warp
Tegunov, D. & Cramer, P · 2019
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MBIR: a cryo-ET 3D reconstruction method that effectively minimizes missing wedge artifacts and restores missing information
Yan, R., Venkatakrishnan, S. V., Liu, J., Bouman, C. A. & Jiang, W · 2019
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A joint deep learning model to recover information and reduce artifacts in missing-wedge sinograms for electron tomography and beyond
Ding, G., Liu, Y., Zhang, R. & Xin, H. L · 2019
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Pytorch: An imperative style, high-performance deep learning library
Paszke, A. et al · 2019
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The promise and the challenges of cryo-electron tomography
Turk, M. & Baumeister, W · 2020
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Tomosipo: fast, flexible, and convenient 3D tomography for complex scanning geometries in Python
Hendriksen, A. A. et al · 2021
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Isotropic reconstruction for electron tomography with deep learning
Liu, Y.-T. et al · 2022
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IDR: self-supervised image denoising via iterative data refinement
Zhang, Y. et al · 2022
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Compressed sensing for electron cryotomography and high-resolution subtomogram averaging of biological specimens
Böhning, J., Bharat, T. A. & Collins, S. M · 2022
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In situ architecture of the ciliary base reveals the stepwise assembly of intraflagellar transport trains
Van den Hoek, H. et al · 2022
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Simultaneous self-supervised reconstruction and denoising of sub-sampled MRI data with Noisier2Noise
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Bendory, T., Bartesaghi, A. & Singer, A · 2020
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Topaz-Denoise: general deep denoising models for cryoEM and cryoET
Bepler, T., Kelley, K., Noble, A. J. & Berger, B · 2020
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Noisier2noise: learning to denoise from unpaired noisy data
Moran, N., Schmidt, D., Zhong, Y. & Coady, P · 2020
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Denoising and regularization via exploiting the structural bias of convolutional generators
Heckel, R. & Soltanolkotabi, M · 2020
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Compressive sensing with un-trained neural networks: gradient descent finds a smooth approximation
Heckel, R. & Soltanolkotabi, M · 2020
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Shrec 2020: classification in cryo-electron tomograms
Gubins, I. et al · 2020
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It’s noisy out there! a review of denoising techniques in cryo-electron tomography
Frangakis, A. S · 2021
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Millard, C. & Chiew, M · 2022
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F2FD: Fourier perturbations for denoising cryo-electron tomograms and comparison to established approaches
Maldonado, J. C. et al · 2023
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A method for restoring signals and revealing individual macromolecule states in cryo-et, REST
Zhang, H. et al · 2023
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Resolving the preferred orientation problem in cryoem reconstruction with self-supervised deep learning
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Cryo-electron tomography: the resolution revolution and a surge of in situ virological discoveries
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