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

Benoît Sauty De Chalon


Currently in my last year of Master in Bioinformatics in Paris, I have come to give this presentation in order to present myself to the lab, my past work and centers of interest. If my profile fits the spirit of the lab, I hope to be able to pursue a Master's internship and then a PhD at the BIG. After a brief overview of the classes I took during my studies and the projects that are relevant to medical imaging and computer vision, I will more specifically expand on the research internship I did last year at the NeuroPoly lab in Montreal, that specializes in spinal cord MRI analysis. My task was to improve the segmentation models in order to be able to perform on data from unseen domains (new acquisition sequence, new scanner, new contrast, etc). To do this, the idea was to give a physical a priori to the model by inputing acquisition metadata along with the image and perform feature wise linear modulations to the feature maps in the segmentation CNN. I will also present some of the initiatives I took outside of this project for the lab workflow. Finally, I will present my interest for the work done at the BIG and why I would like to work there.

Lagrangian Tracking of Bubbles Entrained by a Plunging Jet19 Nov 2019

Alexis Goujon


A liquid jet plunging in a pool of the same liquid may entrain air in the form of bubbles. This process has received much interest in the past due to the fundamental fluid mechanics problems it covers and to its numerous applications, especially in bubble-mediated gas exchange. A study of plunging jets was undertaken focusing on low enough air entrainment rate to enable the individual tracking of bubbles. This Lagrangian point of view gives access to the bubbles' trajectories as well as their residence time, a crucial quantity for gas exchange. While individual bubble motion is essentially random (owing to turbulence), average quantities were found to behave coherently with respect to the jet's parameters. The project comprises experimental work, image processing, bubble tracking and numerical simulations. The presentation will place a special emphasis on the tracking algorithm specially designed for this study.

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

Measure Digital, Reconstruct Analog16 Apr 2019

Julien Fageot
Harvard Harvard John A. Paulson School of Engineering and Applied Sciences

Deep Learning for Non-Linear Inverse Problems02 Apr 2019

Fangshu Yang

Numerical Investigation of Continuous-Domain Lp-norm Regularization in Generalized Interpolation19 Feb 2019

Pakshal Bohra

Inner-Loop-Free ADMM for Cryo-EM15 Jan 2019

Laurène Donati

Fast PET reconstruction: the home stretch11 Dec 2018

Michael McCann

Self-Supervised Deep Active Accelerated MRI27 Nov 2018

Kyong Hwan Jin

Minimum Support Multi-Splines20 Nov 2018

Shayan Aziznejad

Sparse Coding with Projected Gradient Descent for Inverse Problems 23 Oct 2018

Thanh-an Pham

Adversarially-Sandwiched VAEs for Inverse Problems02 Oct 2018

Harshit Gupta

PSF-Extractor: from fluorescent beads measurements to continuous PSF model11 Sep 2018

Emmanuel Soubies

Analysis of Planar Shapes through Shape Dictionary Learning with an Extension to Splines28 Aug 2018

Anna Song

Complex-order scale-invariant operators and self-similar processes21 Aug 2018

Arash Amini
Sharif University, Tehran, Iran

Variational Framework for Continuous Angular Refinement and Reconstruction in Cryo-EM14 Aug 2018

Mona Zehni

Looking beyond Pixels: Theory, Algorithms and Applications of Continuous Sparse Recovery07 Aug 2018

Hanjie Pan
Audiovisual Communications Laboratory (LCAV, EPFL

A L1 representer theorem of vector-valued learning17 Jul 2018

Shayan Aziznejad

Local Rotation Invariance and Directional Sensitivity of 3D Texture Operators: Comparing Classical Radiomics, CNNs and Spherical Harmonics26 Jun 2018

Adrien Depeursinge

Computational Super-Sectioning for Single-Slice Structured-Illumination Microscopy 19 Jun 2018

Emmanuel Soubies

Theoretical and Numerical Analysis of Super-Resolution without Grid19 Jun 2018

Quentin Denoyelle
Université Paris Dauphine

Subdivision-Based Active Contours --- Statistical optimality of Hermite splines for the reconstruction of self-similar signals --- The Role of Discretisation in X-Ray CT Reconstruction29 May 2018

Anaïs Badoual, Virginie Uhlmann, Michael McCann

Influence of spatial context over color perception: unifying chromatic assimilation and simultaneous contrast into a neural field model22 May 2018

Anna Song