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Keywords

Deformable Models Metaheuristics Histological Images Classification Image Registration Global Continuous Optimization Soft Computing Artificial Neural Networks Differential Evolution Stochastic Optimization Evolutionary Algorithms Artificial Neuron-Glia Networks Genetic Algorithms Particle Swarm Optimization Scatter Search Image Segmentation Active Shape Models Artificial Astrocytes Feature Selection Level Set method
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35

Pablo Mesejo

My name is Pablo Mesejo Santiago and I currently hold a Marie Curie Experienced Researcher position at the University of Granada (Spain), one of the top institutions in computer science and engineering.

My principal research areas of interest are computer vision, machine learning and computational intelligence methods applied to image analysis problems (mainly in the biomedical domain). Typical tools I use in my research are stochastic optimization algorithms, deep and shallow neural networks, and ensemble classifiers. During my career I have tackled numerous challenging problems, e.g. the automatic segmentation of anatomical structures in biomedical images (PhD at University of Parma, performed as a Marie Curie Early Stage Researcher, 2010-13), the classification of gastrointestinal lesions from endoscopic videos (postdoc at University of Auvergne Clermont-Ferrand I, 2013-14), the estimation of biophysical parameters from fMRI signals (postdoc at Inria, 2014-16), and the integration of deep learning into probabilistic generative models for visual and audio recognition in human-robot interaction (starting researcher position at Inria, 2016-18).

More information about me and my publications can be found in the following links: Google Scholar, ORCID, Linkedin, DBLP, ResearchGate and ResearcherID.

Journal articles14 documents

  • Stéphane Lathuilière, Pablo Mesejo, Xavier Alameda-Pineda, Radu Horaud. A Comprehensive Analysis of Deep Regression. IEEE Transactions on Pattern Analysis and Machine Intelligence, Institute of Electrical and Electronics Engineers, 2020, 42 (9), pp.2065-2081. ⟨10.1109/TPAMI.2019.2910523⟩. ⟨hal-01754839⟩
  • Oscar Gómez, Pablo Mesejo, Oscar Ibáñez, Andrea Valsecchi, Oscar Cordón. Deep architectures for high-resolution multi-organ chest X-ray image segmentation. Neural Computing and Applications, Springer Verlag, 2019. ⟨hal-02424739⟩
  • Andrea Valsecchi, Javier Irurita Olivares, Pablo Mesejo. Age Estimation in Forensic Anthropology: methodological considerations about the validation studies of prediction models. International Journal of Legal Medicine, Springer Verlag, 2019, 133, pp.1915-1924. ⟨hal-02424740⟩
  • Stéphane Lathuilière, Benoît Massé, Pablo Mesejo, Radu Horaud. Neural Network Based Reinforcement Learning for Audio-Visual Gaze Control in Human-Robot Interaction. Pattern Recognition Letters, Elsevier, 2019, 118, pp.61-71. ⟨10.1016/j.patrec.2018.05.023⟩. ⟨hal-01643775v2⟩
  • Pablo Mesejo, Sandrine Saillet, Olivier David, Christian Bénar, Jan M. Warnking, et al.. A differential evolution-based approach for fitting a nonlinear biophysical model to fMRI BOLD data. IEEE Journal of Selected Topics in Signal Processing, IEEE, 2016, 10 (2), pp.416-427. ⟨10.1109/JSTSP.2015.2502553⟩. ⟨hal-01221115v2⟩
  • Pablo Mesejo, Oscar Ibáñez, Oscar Cordón, Stefano Cagnoni. A Survey on Image Segmentation using Metaheuristic-based Deformable Models: State of the Art and Critical Analysis. Applied Soft Computing, Elsevier, 2016, 44, pp.1-29. ⟨10.1016/j.asoc.2016.03.004⟩. ⟨hal-01282678⟩
  • Pablo Mesejo, Daniel Pizarro, Armand Abergel, Olivier Rouquette, Sylvain Beorchia, et al.. Computer-Aided Classification of Gastrointestinal Lesions in Regular Colonoscopy. IEEE Transactions on Medical Imaging, Institute of Electrical and Electronics Engineers, 2016, 35 (9), pp.2051 - 2063. ⟨10.1109/TMI.2016.2547947⟩. ⟨hal-01291797v2⟩
  • Pablo Mesejo, Andrea Valsecchi, Linda Marrakchi-Kacem, Stefano Cagnoni, Sergio Damas. Biomedical image segmentation using geometric deformable models and metaheuristics. Computerized Medical Imaging and Graphics, Elsevier, 2015, 43, pp.167-178. ⟨10.1016/j.compmedimag.2013.12.005⟩. ⟨hal-01221316v3⟩
  • Pablo Mesejo, Óscar Ibáñez, Enrique Fernández-Blanco, Francisco Cedrón, Alejandro Pazos, et al.. Artificial Neuron–Glia Networks Learning Approach Based on Cooperative Coevolution. International Journal of Neural Systems, World Scientific Publishing, 2015, 25 (4), ⟨10.1142/S0129065715500124⟩. ⟨hal-01221226⟩
  • Pablo Mesejo. Automatic Segmentation of Anatomical Structures using Deformable Models and Bio-Inspired/Soft Computing. Electronic Letters on Computer Vision and Image Analysis, Computer Vision Center Press, 2014, 13 (2). ⟨hal-01221335⟩
  • Roberto Ugolotti, Youssef S.G. Nashed, Pablo Mesejo, Spela Ivekovič, Luca Mussi, et al.. Particle Swarm Optimization and Differential Evolution for model-based object detection. Applied Soft Computing, Elsevier, 2013, 13 (6), pp.3092-3105. ⟨10.1016/j.asoc.2012.11.027⟩. ⟨hal-01221292⟩
  • Roberto Ugolotti, Pablo Mesejo, Samantha Zongaro, Barbara Bardoni, Gaia Berto, et al.. Visual Search of Neuropil-Enriched RNAs from Brain In Situ Hybridization Data through the Image Analysis Pipeline Hippo-ATESC. PLoS ONE, Public Library of Science, 2013, 8 (9), ⟨10.1371/journal.pone.0074481⟩. ⟨hal-01221314⟩
  • Pablo Mesejo, Roberto Ugolotti, Ferdinando Di Cunto, Mario Giacobini, Stefano Cagnoni. Automatic Hippocampus Localization in Histological Images using Differential Evolution-Based Deformable. Pattern Recognition Letters, Elsevier, 2012, 34 (3), pp.299-307. ⟨hal-01221303⟩
  • Ana B. Porto-Pazos, Noha Veiguela, Pablo Mesejo, Marta Navarrete, Alberto Alvarellos, et al.. Artificial Astrocytes Improve Neural Network Performance. PLoS ONE, Public Library of Science, 2011, 6 (4), ⟨10.1371/journal.pone.0019109⟩. ⟨hal-01221307⟩

Conference papers17 documents

  • Sylvain Guy, Stéphane Lathuilière, Pablo Mesejo, Radu Horaud. Learning Visual Voice Activity Detection with an Automatically Annotated Dataset. ICPR 2020 - 25th International Conference on Pattern Recognition, Jan 2021, Milano, Italy. pp.1-6. ⟨hal-02882229v4⟩
  • Benoit Massé, Stéphane Lathuilière, Pablo Mesejo, Radu Horaud. Extended Gaze Following: Detecting Objects in Videos Beyond the Camera Field of View. FG 2019 - 14th IEEE International Conference on Automatic Face and Gesture Recognition, May 2019, Lille, France. pp.1-8, ⟨10.1109/FG.2019.8756555⟩. ⟨hal-02054236⟩
  • Mariia Vladimirova, Jakob Verbeek, Pablo Mesejo, Julyan Arbel. Understanding Priors in Bayesian Neural Networks at the Unit Level. ICML 2019 - 36th International Conference on Machine Learning, Jun 2019, Long Beach, United States. pp.6458-6467. ⟨hal-02177151⟩
  • Mariia Vladimirova, Julyan Arbel, Pablo Mesejo. Bayesian neural network priors at the level of units. AABI 2018 - 1st Symposium on Advances in Approximate Bayesian Inference, Dec 2018, Montréal, Canada. pp.1-6. ⟨hal-01950659⟩
  • Mariia Vladimirova, Julyan Arbel, Pablo Mesejo. Bayesian neural networks become heavier-tailed with depth. NeurIPS 2018 - Thirty-second Conference on Neural Information Processing Systems, Dec 2018, Montréal, Canada. pp.1-7. ⟨hal-01950658⟩
  • Stéphane Lathuilière, Pablo Mesejo, Xavier Alameda-Pineda, Radu Horaud. DeepGUM: Learning Deep Robust Regression with a Gaussian-Uniform Mixture Model. ECCV 2018 - European Conference on Computer Vision, Sep 2018, Munich, Germany. pp.205-221, ⟨10.1007/978-3-030-01228-1_13⟩. ⟨hal-01851511⟩
  • Stéphane Lathuilière, Benoit Massé, Pablo Mesejo, Radu Horaud. Deep Reinforcement Learning for Audio-Visual Gaze Control. IROS 2018 - IEEE/RSJ International Conference on Intelligent Robots and Systems, Oct 2018, Madrid, Spain. pp.1555-1562, ⟨10.1109/IROS.2018.8594327⟩. ⟨hal-01851738⟩
  • Stéphane Lathuilière, Rémi Juge, Pablo Mesejo, Rafael Muñoz-Salinas, Radu Horaud. Deep Mixture of Linear Inverse Regressions Applied to Head-Pose Estimation. IEEE Conference on Computer Vision and Pattern Recognition, Jul 2017, Honolulu, Hawaii, United States. pp.7149-7157, ⟨10.1109/CVPR.2017.756⟩. ⟨hal-01504847⟩
  • Pablo Mesejo, Sandrine Saillet, Olivier David, Christian Bénar, Jan M. Warnking, et al.. Estimating Biophysical Parameters from BOLD Signals through Evolutionary-Based Optimization. 18th International Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI’15), Oct 2015, Munich, Germany. pp.528-535, ⟨10.1007/978-3-319-24571-3_63⟩. ⟨hal-01221126⟩
  • Andrea Valsecchi, Pablo Mesejo, Linda Marrakchi-Kacem, Stefano Cagnoni, Sergio Damas. Automatic evolutionary medical image segmentation using deformable models. 16th IEEE Congress on Evolutionary Computation (CEC’14), Jul 2014, Beijing, China. pp.97-104, ⟨10.1109/CEC.2014.6900466⟩. ⟨hal-01221343⟩
  • Toby Collins, Pablo Mesejo, Adrien Bartoli. An Analysis of Errors in Graph-Based Keypoint Matching and Proposed Solutions. 13th European Conference on Computer Vision (ECCV’14), Sep 2014, Zurich, Switzerland. pp.138-153, ⟨10.1007/978-3-319-10584-0_10⟩. ⟨hal-01221328⟩
  • Roberto Ugolotti, Pablo Mesejo, Youssef S.G. Nashed, Stefano Cagnoni. GPU-Based Automatic Configuration of Differential Evolution: A Case Study. 16th Portuguese Conference on Artificial Intelligence, EPIA 2013, Sep 2013, Azores, Portugal. pp.114-125, ⟨10.1007/978-3-642-40669-0_11⟩. ⟨hal-01221512⟩
  • Roberto Ugolotti, Youssef S.G. Nashed, Pablo Mesejo, Stefano Cagnoni. Algorithm configuration using GPU-based metaheuristics. 15th Genetic and Evolutionary Computation Conference companion (GECCO’13), Jul 2013, Amsterdam, Netherlands. pp.221-222, ⟨10.1145/2464576.2464682⟩. ⟨hal-01221570⟩
  • Pablo Mesejo, Stefano Cagnoni, Alessandro Costalunga, Davide Valeriani. Segmentation of histological images using a metaheuristic-based level set approach. 15th Genetic and Evolutionary Computation Conference companion (GECCO’13), Jul 2013, Amsterdam, Netherlands. pp.1455-1462, ⟨10.1145/2464576.2466808⟩. ⟨hal-01221602⟩
  • Pablo Mesejo, Stefano Cagnoni. An experimental study on the automatic segmentation of in situ hybridization-derived images. 1st International Conference on Medical Imaging using Bio-Inspired and Soft Computing (MIBISOC’13), May 2013, Brussels, Belgium. pp.153-160. ⟨hal-01221613⟩
  • Youssef S.G. Nashed, Pablo Mesejo, Roberto Ugolotti, Jérémie Dubois-Lacoste, Stefano Cagnoni. A Comparative Study of Three GPU-Based Metaheuristics. 12th International Conference on Parallel Problem Solving from Nature (PPSN’12), Sep 2012, Taormina, Italy. pp.398-407, ⟨10.1007/978-3-642-32964-7_40⟩. ⟨hal-01221645⟩
  • Pablo Mesejo, Roberto Ugolotti, Ferdinando Di Cunto, Stefano Cagnoni, Mario Giacobini. Automatic segmentation of hippocampus in histological images of mouse brains using deformable models and random forest. 25th IEEE International Symposium on Computer-Based Medical Systems (CBMS’12), Jun 2012, Rome, Italy. pp.1-4, ⟨10.1109/CBMS.2012.6266318⟩. ⟨hal-01221660⟩

Poster communications1 document

  • Mariia Vladimirova, Julyan Arbel, Pablo Mesejo. Bayesian neural network priors at the level of units. Bayesian Statistics in the Big Data Era, Nov 2018, Marseille, France. pp.1. ⟨hal-01950660⟩

Book sections1 document

  • Carlos Fernandez-Lozano, Jose A. Seoane, Pablo Mesejo, Youssef S.G. Nashed, Stefano Cagnoni, et al.. Texture Classification of Proteins Using Support Vector Machines and Bio-inspired Metaheuristics. Biomedical Engineering Systems and Technologies, 452, pp.117-130, 2014, 978-3-662-44485-6. ⟨10.1007/978-3-662-44485-6_9⟩. ⟨hal-01221496⟩

Preprints, Working Papers, ...1 document

  • Mariia Vladimirova, Julyan Arbel, Pablo Mesejo. Bayesian neural networks increasingly sparsify their units with depth. 2018. ⟨hal-01950657⟩

Theses1 document

  • Pablo Mesejo. AUTOMATIC SEGMENTATION OF ANATOMICAL STRUCTURES USING DEFORMABLE MODELS AND BIO-INSPIRED/SOFT COMPUTING. Artificial Intelligence [cs.AI]. University of Parma, 2014. English. ⟨tel-01363683⟩