Pablo Mesejo holds a starting researcher position in the PERCEPTION team at Inria from September 2016. From July 2010 to July 2013 he was employed as Early Stage Researcher within the Marie Curie ITN MIBISOC (“Medical Imaging using Bio-Inspired and SOft Computing”) at the Department of Information Engineering of the University of Parma, where he also worked as teaching assistant. His PhD dissertation was focused on the automatic segmentation of anatomical structures in biomedical images. After that, from September 2013 to August 2014, he was a postdoctoral researcher at the ALCoV team (Advanced Laparoscopy and Computer Vision) of the University of Auvergne Clermont-Ferrand I. The work developed was mainly focused on deformable feature-based image registration and 3D measurement and characterization of neoplasias from endoscopic videos in gastroenterology. Later, he moved to the MISTIS team (Modeling and inference of complex and structured stochastic systems) of Inria Grenoble Rhône-Alpes to work as postdoctoral researcher in the estimation of biophysical parameters from fMRI signals through evolutionary-based optimization to study brain function.
His main research interests are Biomedical Image Analysis, Soft Computing (specially metaheuristics and artificial neural networks), Machine Learning (mainly discriminative models), and Computer Vision (particularly image segmentation and registration). His current work is mainly related with the integration of deep learning into probabilistic generative models for visual and audio recognition.
Mesejo, P., Pizarro, D., Abergel, A., Rouquette, O., Beorchia, S., Poincloux, L., and Bartoli, A., “Computer-Aided Classification of Gastrointestinal Lesions in Regular Colonoscopy”, IEEE Transactions on Medical Imaging 35 (9), 2051-2063, IEEE, September – 2016
Mesejo, P., Ibáñez, O., Cordón, O., and Cagnoni, S., “A Survey on Image Segmentation using Metaheuristic-based Deformable Models: State of the Art and Critical Analysis”, Applied Soft Computing 44, 1-29, Elsevier, July – 2016
Mesejo, P., Saillet, S., David, O., Bénar, C., Warnking, J. M., and Forbes, F., “A Differential Evolution-based Approach for Fitting a Nonlinear Biophysical Model to fMRI BOLD Data”, IEEE Journal of Selected Topics in Signal Processing 10 (2), 416-427, IEEE, March – 2016
Mesejo, P., Saillet, S., David, O., Bénar, C., Warnking, J.M., and Forbes, F., “Estimating biophysical parameters from BOLD signals through evolutionary-based optimization”, 18th International Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI’15), Part II, 528-535, Munich, October – 2015
Mesejo, P., Valsecchi, A., Marrakchi-Kacem, L., Cagnoni, S., and Damas, S., “Biomedical Image Segmentation using Geometric Deformable Models and Metaheuristics”, Computerized Medical Imaging and Graphics 43, 167-178, Elsevier, July – 2015
Mesejo, P., Ibáñez, O., Fernández-Blanco, E., Cedrón, F., Pazos, A., and Porto-Pazos, A., “Artificial Neuron-Glia Networks Learning Approach Based On Cooperative Coevolution”, International Journal of Neural Systems 25 (4), doi: 10.1142/S0129065715500124, World Scientific, April – 2015
Collins, T., Mesejo, P., and Bartoli, A., “An Analysis of Errors in Graph-based Keypoint Matching and Proposed Solutions”, 13th European Conference on Computer Vision (ECCV’14), 138-153, Zürich, September – 2014
Ugolotti, R.*, Mesejo, P.*, Zongaro, S., Bardoni, B., Berto, G., Bianchi, F., Molineris, I., Giacobini, M., Cagnoni, S., and Di Cunto, F., ”Visual search of neuropil-enriched RNAs from brain in situ hybridization data through the image analysis pipeline Hippo-ATESC”, PLoS ONE 8 (9): e74481.doi:10.1371/journal.pone.0074481, Public Library of Science, September – 2013 *These authors contributed equally to this work.
Ugolotti, R., Nashed, Y.S.G., Mesejo, P., Ivekovič, S., Mussi, L., and Cagnoni, S., “Particle Swarm Optimization and Differential Evolution for Model-based Object Detection”, Applied Soft Computing 13 (6), 3092-3105, Elsevier, June – 2013
Mesejo, P., Ugolotti, R., Di Cunto, F., Giacobini, M., and Cagnoni, S., “Automatic Hippocampus Localization in Histological Images using Differential Evolution-Based Deformable Models”, Pattern Recognition Letters 34 (3), 299-307, Elsevier, February – 2013
Nashed, Y.S.G., Mesejo, P., Ugolotti, R., Dubois-Lacoste, J., Cagnoni, S., “A comparative study of three GPU-based metaheuristics”, 12th International Conference on Parallel Problem Solving from Nature (PPSN’12), 398-407, Taormina, September – 2012
Porto-Pazos, A., Veiguela, N., Mesejo, P., Navarrete, M., Alvarellos, A., Ibáñez, O., Munteanu, C., Pazos, A., and Araque, A., “Artificial Astrocytes Improve Neural Network Performance”, PLoS ONE 6 (4): e19109, doi:10.1371/journal.pone.0019109, Public Library of Science, April – 2011