Graphic STI
logo EPFL
text EPFL
english only
Biomedical Imaging Group
EPFL > BIG > SeminarsLogin

Home page

News & Events





Tutorials & Reviews

Recent Talks


Download Algorithms

Jobs and Trainees


Student Projects


Order by     

Inverse Problems with Fourier-Domain Measurements and gTV Regularization:

 uniqueness and reconstruction algorithm22 Sep 2020

Thomas Debarre

Time-dependent deep image prior for dynamic MRI08 Sep 2020

Jaejun Yoo

Shortest Multi-spline Bases for Generalized Sampling03 Aug 2020

Alexis Goujon

Convex Optimization in Infinite Sums of Banach Spaces Using Besov Regularization13 Jul 2020

Benoît Sauty De Chalon

Measuring Complexity of Deep Neural Networks29 Jun 2020

Shayan Aziznejad

Robust Phase Unwrapping via Deep Image Prior for Quantitative Phase Imaging22 Jun 2020

Fangshu Yang

Space Varying Blurs: Estimation, Identification and Applications18 May 2020

Valentin Debarnot

Matrix factorization and phase retrieval for deep fluorescence microscopy11 May 2020

Jonathan Dong

CryoGAN: A New Reconstruction Paradigm for Single-particle Cryo-EM Via Deep Adversarial Learning27 Apr 2020

Harshit Gupta

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

Laurène Donati

Gibbs Sampling-Based Statistical Inference for Inverse Problems20 Apr 2020

Pakshal Bohra

Rethinking Data Augmentation for Low-level Vision Tasks: A Comprehensive Analysis and A New Strategy "CutBlur"23 Mar 2020

Jaejun Yoo

Robust Reconstruction of Fluorescence Molecular Tomography With An Optimized Illumination Pattern04 Mar 2020

Yan Liu

Solving various domain translation problems using deep convolutional framelets11 Feb 2020

Jaejun Yoo

Adaptive regularization for three-dimensional optical diffraction tomography17 Dec 2019

Thanh-An Pham

About the use of non-imaging data to improve domain adaptation for spinal cord segmentation on MRI26 Nov 2019

Benoît Sauty De Chalon

Lagrangian Tracking of Bubbles Entrained by a Plunging Jet19 Nov 2019

Alexis Goujon

Multigrid Methods for Helmholtz equation and its application in Optical Diffraction Tomography05 Nov 2019

Tao Hong
Department of Computer Science, Technion – Israel Institute of Technology

Efficient methods for solving large scale inverse problems17 Oct 2019

Eran Treister
Computer Science Department at Ben Gurion University of the Negev, Beer Sheva, Israel

Generating Sparse Stochastic Processes24 Sep 2019

Leello Tadesse Dadi

Sparse signal reconstruction using variational methods with fractional derivatives10 Sep 2019

Stefan Stojanovic

Multivariate Haar wavelets and B-splines13 Aug 2019

Tanya Zaitseva

Deep Learning for Magnetic Resonance Image Reconstruction and Analysis06 Aug 2019

Chen Qin

The Interpolation Problem with TV(2) Regularization30 Jul 2019

Thomas Debarre

Duality and Uniqueness for the gTV problem.23 Jul 2019

Quentin Denoyelle

An Introduction to Convolutional Neural Networks for Inverse Problems in Imaging09 Jul 2019

Harshit Gupta

Multiple Kernel Regression with Sparsity Constraints18 Jun 2019

Shayan Aziznejad

Optimal Spline Generators for Derivative Sampling18 Jun 2019

Shayan Aziznejad

Total variation minimization through Domain Decomposition28 May 2019

Vasiliki Stergiopoulou

Cell detection by functional inverse diffusion and non-negative group sparsity07 May 2019

Pol del Aguila Pla
KTH Royal Institute of Technology, Division of Information Science and Engineering, School of Electrical Engineering and Computer Science

Can neural networks always be trained? On the boundaries of deep learning06 May 2019

Matt J. Colbrook
Department of Applied Mathematics and Theoretical Physics (DAMTP), University of Cambridge

© 2010 EPFL • • 26.01.2010