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Seminars |
Convex Optimization in Infinite Sums of Banach Spaces Using Besov Regularization13 Jul 2020
Measuring Complexity of Deep Neural Networks29 Jun 2020
Robust Phase Unwrapping via Deep Image Prior for Quantitative Phase Imaging22 Jun 2020
Space Varying Blurs: Estimation, Identification and Applications18 May 2020
Matrix factorization and phase retrieval for deep fluorescence microscopy11 May 2020
CryoGAN: A New Reconstruction Paradigm for Single-particle Cryo-EM Via Deep Adversarial Learning27 Apr 2020
In this talk, we present CryoGAN, a new paradigm for single-particle cryo-EM reconstruction based on unsupervised deep adversarial learning. The major challenge in single-particle cryo-EM is that the measured particles have unknown poses. Current reconstruction techniques either estimate the poses or marginalize them away—steps that are computationally challenging. CryoGAN sidesteps this problem by using a generative adversarial network (GAN) to learn the 3D structure whose simulated projections most closely match the real data in a distributional sense. The architecture of CryoGAN resembles that of standard GAN, with the twist that the generator network is replaced by a cryo-EM physics simulator. CryoGAN is an unsupervised algorithm that only demands picked particle images and CTF estimation as inputs; no initial volume estimate or prior training are needed. Moreover, it requires minimal user interaction and can provide reconstructions in a matter of hours on a high-end GPU. The current results on synthetic datasets show that the CryoGAN can reconstruct a high-resolution volume with its adversarial learning scheme. Preliminary results on real β-galactosidase data demonstrate its ability to capture and exploit real data statistics in more challenging imaging conditions. If the time permits, we would also like to discuss its extension for multiple conformations.
CryoGAN: A New Reconstruction Paradigm for Single-particle Cryo-EM Via Deep Adversarial Learning27 Apr 2020
Gibbs Sampling-Based Statistical Inference for Inverse Problems20 Apr 2020
Robust Reconstruction of Fluorescence Molecular Tomography With An Optimized Illumination Pattern04 Mar 2020
Solving various domain translation problems using deep convolutional framelets11 Feb 2020
Adaptive regularization for three-dimensional optical diffraction tomography17 Dec 2019
About the use of non-imaging data to improve domain adaptation for spinal cord segmentation on MRI26 Nov 2019
Lagrangian Tracking of Bubbles Entrained by a Plunging Jet19 Nov 2019
Multigrid Methods for Helmholtz equation and its application in Optical Diffraction Tomography05 Nov 2019
Efficient methods for solving large scale inverse problems17 Oct 2019
Generating Sparse Stochastic Processes24 Sep 2019
Sparse signal reconstruction using variational methods with fractional derivatives10 Sep 2019
Multivariate Haar wavelets and B-splines13 Aug 2019
Deep Learning for Magnetic Resonance Image Reconstruction and Analysis06 Aug 2019
The Interpolation Problem with TV(2) Regularization30 Jul 2019
Duality and Uniqueness for the gTV problem.23 Jul 2019
An Introduction to Convolutional Neural Networks for Inverse Problems in Imaging09 Jul 2019
Multiple Kernel Regression with Sparsity Constraints18 Jun 2019
Optimal Spline Generators for Derivative Sampling18 Jun 2019
Total variation minimization through Domain Decomposition28 May 2019
Cell detection by functional inverse diffusion and non-negative group sparsity07 May 2019
Can neural networks always be trained? On the boundaries of deep learning06 May 2019
Measure Digital, Reconstruct Analog16 Apr 2019
Deep Learning for Non-Linear Inverse Problems02 Apr 2019
Numerical Investigation of Continuous-Domain Lp-norm Regularization in Generalized Interpolation19 Feb 2019
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