Description du projet
De nouveaux algorithmes pour manipuler de grandes quantités de données en traitement d’images
Plusieurs domaines scientifiques comme la biologie, la médecine, la physique et la chimie produisent une quantité de plus en plus importante de données qui doivent être traitées et stockées de manière efficace. Trier ce torrent de données impose de concevoir de nouveaux algorithmes informatiquement extensibles, robustes et polyvalents. Le framework de majoration-minimisation (MM), qui utilise une classe d’algorithmes d’optimisation efficaces et efficients, joue un rôle crucial dans le traitement optimisé des données. Le projet MAJORIS, financé par l’UE, permettra de concevoir de nouveaux algorithmes MM capables de rester efficaces lorsqu’il s’agit de traiter des données volumineuses. Les travaux entrepris incluront l’élaboration de stratégies d’accélération et l’analyse de la convergence. Les applications visées sont la microscopie multiphoton à haute résolution, la reconstruction d’images de tomosynthèse mammaire et le traitement de données de spectrométrie de masse.
Objectif
Mathematical optimization is the key to solving many problems in science, based on the observation that physical systems obey a general principle of least action. While some problems can be solved analytically, many more can only be solved via numerical algorithms. Research in this domain has proved essential over many years. In addition, science in general is changing. Increasingly, in biology, medicine, astronomy, chemistry, physics, large amounts of data are collected by constantly improving signal and image acquisition devices, that must be analyzed by sophisticated optimization tools. In this proposal, we consider handling optimization problems with large datasets. This means minimizing a cost function with a complex structure and many variables. The computational load for solving these problems is too great for even state-of-the-art algorithms. Thus, only relatively rudimentary data processing techniques are employed, reducing the quality of the results and limiting the outcomes that can be achieved via these novel instruments. New algorithms must be designed with computational scalability, robustness and versatility in mind.
In this context, Majorization-Minimization (MM) approaches have a crucial role to play. They consist of a class of efficient and effective optimization algorithms that benefit from solid theoretical foundations. The MAJORIS project aims at proposing a breakthrough in MM algorithms, so that they remain efficient when dealing with big data. I propose to tackle several challenging questions concerning algorithm design. These include acceleration strategies, convergence analysis with complex costs and inexact schemes. I will also tackle practical, massively parallel and distributed architecture implementations. Three specific applications are targeted: super-resolution in multiphoton microscopy in biology; on-the-fly reconstruction for 3D breast tomosynthesis in medical imaging; and mass spectrometry data processing in chemistry.
Champ scientifique
- natural sciencescomputer and information sciencesdata sciencebig data
- natural sciencesphysical sciencesopticsmicroscopysuper resolution microscopy
- natural sciencesphysical sciencesastronomy
- natural scienceschemical sciencesanalytical chemistrymass spectrometry
- natural sciencescomputer and information sciencesdata sciencedata processing
Programme(s)
Thème(s)
Régime de financement
ERC-STG - Starting GrantInstitution d’accueil
78153 Le Chesnay Cedex
France