Séminaire Performance et Généricité

From LRDE

Revision as of 12:47, 29 February 2016 by Akim (talk | contribs) (À propos du séminaire: Address.)


À propos du séminaire

La modélisation orientée objet permet la classification des problèmes de calcul scientifique, et par conséquent, par la factorisation qu'elle rend possible, elle fournit un excellent support pour la fédération d'efforts de développement. Malheureusement les performances en pâtissent souvent. De nouveaux langages, de nouvelles techniques de programmation réconcilient performance et généricité, permettant la naissance de bibliothèques de nouvelle génération (Boost, Olena, Vcsn, etc.).

L'objet de ce séminaire est la diffusion du savoir et des compétences sur la modélisation de bibliothèques métiers génériques et performantes.

Mots clés: Calcul Scientifique, Distribution, Génie Logiciel, Généricité, Grille, Langages, Multi-cœur, Paradigmes de Programmation, Parallélisme, Recherche reproductible.

Comment venir: Contact.

Prochaines séances

Archives

Mercredi 10 avril 2019, 11h - 12h, Amphi 4

Deep Learning for Satellite Imagery: Semantic Segmentation, Non-Rigid Alignment, and Self-Denoising

Guillaume Charpiat (Équipe TAU, INRIA Saclay / LRI - Université Paris-Sud)

Neural networks have been producing impressive results in computer vision these last years, in image classification or segmentation in particular. To be transferred to remote sensing, this tool needs adaptation to its specifics: large images, many small objects per image, keeping high-resolution output, unreliable ground truth (usually mis-registered). We will review the work done in our group for remote sensing semantic segmentation, explaining the evolution of our neural net architecture design to face these challenges, and finally training a network to register binary cadaster maps to RGB images while detecting new buildings if any, in a multi-scale approach. We will show in particular that it is possible to train on noisy datasets, and to make predictions at an accuracy much better than the variance of the original noise. To explain this phenomenon, we build theoretical tools to express input similarity from the neural network point of view, and use them to quantify data redundancy and associated expected denoising effects. If time permits, we might also present work on hurricane track forecast from reanalysis data (2-3D coverage of the Earth's surface with temperature/pressure/etc. fields) using deep learning.

After a PhD thesis at ENS on shape statistics for image segmentation, and a year in Bernhard Schölkopf's team at MPI Tübingen on kernel methods for medical imaging, Guillaume Charpiat joined INRIA Sophia-Antipolis to work on computer vision, and later INRIA Saclay to work on machine learning. Lately, he has been focusing on deep learning, with in particular remote sensing imagery as an application field.

https://www.lri.fr/~gcharpia/



Mercredi 6 mars 2019, 11h - 12h, Amphi 4

Restauration de la vision grâce aux implants rétiniens

Vincent Bismuth (GEHC)

Rendre la vue à ceux qui l’ont perdue a longtemps été considéré comme un sujet réservé à la science-fiction. Cependant, sur les vingt dernières années les efforts intensifiés dans le domaine des prothèses visuelles ont abouti à des avancées significatives, et plusieurs centaines de patients dans le monde ont reçu de tels dispositifs. Ce séminaire présentera brièvement le domaine des prothèses rétiniennes avec une focalisation particulière sur les aspects de traitement d’image. Nous exposerons les principales approches, les limitations connues et les résultats.

Vincent Bismuth mène une carrière dans le domaine du traitement d’image pour les dispositifs médicaux. Il a contribué pendant plus de dix ans au développement d’algorithmes de traitement d’image et de vidéos pour les procédures chirurgicales interventionnelles chez GE Healthcare. Il s’est ensuite consacré pendant quatre ans à la conception de systèmes de restauration visuelle pour les malvoyants dans la start-up Pixium Vision. Fin 2018, il a rejoint la division mammographie de GE Healthcare où il mène des développements en traitement d’image.



Vendredi 14 décembre 2018, 11h-12h, Amphi IP12A

Toward myocardium perfusion from X-ray CT

Clara Jaquet (ESIEE Marne-la-Vallée)

Recent advances in medical image computing have resulted in automated systems that closely assist physicians in patient therapy. Computational and personalized patient models benefit diagnosis, prognosis and treatment planning, with a decreased risk for the patient, as well as potentially lower cost. HeartFlow Inc. is a successful example of a company providing such a service in the cardiovascular context. Based on patient-specific vascular model extracted from X-ray CT images, they identify functionally significant disease in large coronary arteries. Their combined anatomical and functional analysis is nonetheless limited by the image resolution. At the downstream scale, a functional exam called Myocardium Perfusion Imaging (MPI) highlights myocardium regions with blood flow deficit. However, MPI does not functionally relate perfusion to the upstream coronary disease. The goal of our project is to build the functional bridge between coronary and myocardium. To this aim we propose an anatomical and functional extrapolation. We produce an innovative vascular network generation method extending the coronary model down to the microvasculature. In the resulting vascular model, we compute a functional analysis pipeline to simulate flow from large coronaries to the myocardium, and to enable comparison with MPI ground-truth data.

After completing a technological university degree in biology at Creteil, Clara Jaquet obtained the diploma of biomedical engineer from ISBS (Bio-Sciences Institute) in 2015. She worked for one year at HeartFlow Inc, California, before starting a PhD at ESIEE, Université Paris-Est, within the LIGM laboratory, on a research project jointly with the same company.



more…


Contact

Liens