Biomedical signal and image processing challenges, tools and open problems
Signal and image processing has always been the main tools in Medical and Biomedical applications. Nowadays, there are great number of toolboxes, general purpose and very specialized, in which, classical and advanced techniques can be used. All the transformation methods (Fourier, Wavelets, Radon, Abel, ... and much more) as well as all the Model Based and iterative regularization methods. Statistical and Bayesian inference based methods also had many success, in particular, when there is less data, noisy, uncertain, mock and outliers data and there is a need to account and to quantify uncertainties.
In some applications, nowadays, we have more and more data: "Big Data". To use these huge data to extract more knowledge, the Machine Learning and Artificial Intelligence tools have shown success and became mandatory.
In this keynote talk, I give an overview and a survey of the aforementioned methods
Ali Mohammad-Djafari received the B.Sc. in electrical engineering from Polytechnic of Teheran, in 1975, the M.Sc. from Supélec (Now CentraleSupélec) in 1977, the "Docteur-Ingénieur" (Ph.D.) and "Doctorat d'Etat" in Physics, from the University of Paris Sud 11 (UPS), Orsay, France, respectively in 1981 and 1987.
He was Assistant Professor at UPS for two years (1981-1983). Since 1984 until 2018, he had a permanent position at CNRS. He was a visiting Associate Professor at University of Notre Dame, Indiana, USA during 1997-1998. He is now retired from CNRS and founded the International Science Consulting & Training (ISCT): https://sites.google.com/view/isct
His main scientific interests are in developing new probabilistic methods based on Bayesian inference, Information Theory and Maximum Entropy approaches for Inverse Problems in general in all aspects of data processing, and more specifically in imaging and vision systems: image reconstruction, signal and image deconvolution, blind source separation, sources localization, data fusion, multi and hyper spectral image segmentation. The main application domains of his interests are Medical imaging, Computed Tomography (X rays, PET, SPECT, MRI, microwave, ultrasound and eddy current imaging) either for medical imaging or for Non Destructive Testing (NDT) in industry, multivariate and multi dimensional data, space-time signal and image processing, data mining, clustering, classification and machine learning methods for biological or medical applications.
He has supervised more than 20 Ph.D. Thesis, more than 20 Post-doc research activities and more than 50 M.Sc. Student research projects. He has more than 400 papers in journals, national and international conferences. He has organized more than 10 international workshops and conferences. He has been expert for a great number of French national and international projects. Since 1988 he has many teaching activities in M.Sc. and Ph.D. Level in SUPELEC, University of Paris Sud, ENSTA, Ecole centrale de Paris and Université Paris Saclay.
He also participated and managed many industrial contracts with many French national industries such as EDF, RENAULT, THALES, SAFRAN and great research institutions such as CEA, INSERM, INRIA as well as the regional (such as Digiteo), national (such as ANR) and European projects (such as ERASYSBIO).