Xavier P. Burgos-Artizzu

Most representative publications

 A.I. applied to medicine (2016-)

Low levels of CIITA and high levels of SOCS1 predict COVID-19 disease severity in children and adults. Monica Girona-Alarcon; Guillermo Argüello; Ana Esteve-Sole; Sara Bobillo-Perez; Xavier P. Burgos-Artizzu; Elisenda Bonet-Carne; Anna Mensa-Vilaró; Anna Codina; Maria Hernandez-Garcia; Cristina Jou; Laia Alsina; Iolanda Jordan. iScience. 25 – 1, Elsevier, 2022.

Noninvasive prediction models of intra-amniotic infection in women with preterm labor. Teresa Cobo; Xavier P. Burgos-Artizzu; Mari Carmen Collado; Vicente Andreu-Fernandez; Ana B. Sanchez-Garcia; Xavier Filella; Silvia Marin; Marta Cascante; Jordi Bosch; Silvia Ferrero; David Boada; Clara
Murillo; Claudia Rueda; Julia Ponce; Montse Palacio; Eduard Gratacos. American Journal of Obstetrics & Gynecology. Elsevier, 2022

Analysis of maturation features in fetal brain ultrasound via artificial intelligence for the estimation of gestational age. X. P. Burgos-Artizzu, D. Coronado-Gutiérrez, B. Valenzuela-Alcaraz, K. Vellve, E. Eixarch, F. Crispi, E. Bonet-Carne, M. Bennasar, and E. Gratacós. American Journal of Obstetrics and Gynecology, Maternal-Fetal Medicine (AJOG-MFM), 2021

Automated covid-19 detection from frontal chest x-ray images using deep learning: an online feasibility study. X. P. Burgos-Artizzu. medRxiv, 2020

Evaluation of deep convolutional neural networks for automatic classification of common maternal fetal ultrasound planes. X. P. Burgos-Artizzu, D. Coronado-Gutiérrez, B. Valenzuela-Alcaraz, E. Bonet-Carne,E. Eixarch, F. Crispi, and E. Gratacós. Nature Scientific Reports, 10(1):10200, 2020

Evaluation of an improved tool for non-invasive prediction of neonatal respiratory morbidity based on fully automated fetal lung ultrasound analysis. X. P. Burgos-Artizzu, Á. Perez-Moreno, D. Coronado-Gutierrez, E. Gratacos, and M. Palacio. Nature Scientific Reports, 9(1):1950, 2019

Quantitative ultrasound image analysis of axillary lymph nodes to diagnose metastatic involvement in breast cancer. D. Coronado-Gutirrez, G. Santamara, S. Ganau, X. Bargallo, S. Orlando, M. Oliva Braas, A. Perez-Moreno, and X. Burgos-Artizzu. Ultrasound Med Biol, 11(45):2932–2941, 2019

Facial recognition (2010-2016)

Pose and expression coherent face recovery in the wild. X. P. Burgos-Artizzu, J. Zepeda, F. Le Clerc, and P. Perez. In ICCV Workshops, 2015

Real-time expression-sensitive hmd face reconstruction. X. P. Burgos-Artizzu, J. Fleureau, O. Dumas, T. Thapie, F. Le Clerc, and N. Mollet. In SIGGRAPH Asia, 2015

Distance estimation of an unknown person from a portrait. X. P. Burgos-Artizzu, M. R. Ronchi, and P. Perona. In ECCV, 2014

Robust face landmark estimation under occlusion. X. P. Burgos-Artizzu, P. Perona, and P. Dollár. In ICCV, 2013

Behavior recognition from video (2010-2016)

Three-dimensional pose estimation for laboratory mouse from monocular images. G. Salem, J. Krynitsky, M. Hayes, T. Pohida, and X. Burgos-Artizzu. IEEE Transactions on Image Processing, 28(9):4273–4287, 2019

Automated measurement of mouse social behaviors using depth sensing, video tracking, and machine learning. W. Hong, A. Kennedy, X. Burgos-Artizzu, M. Zelikowsky, S. Navonne, P. Perona, and D. Anderson. Proc. National Academy of Sciences USA (PNAS), 112(38):E5351–E5360, 2015

Detecting social actions of fruit flies. E. Eyjolfsdottir, S. Branson, X. Burgos-Artizzu, E. Hoopfer, J. Schor, D. Anderson, and P. Perona.  In ECCV, 2014

Social behavior recognition in continuous videos. X. P. Burgos-Artizzu, P. Dollár, D. Lin, D. Anderson, and P. Perona. In CVPR, 2012

A.I. in agriculture (2006-2012)

Real-time image processing for crop/weed discrimination in maize fields. X. P. Burgos-Artizzu, A. Ribeiro, M. Guijarro, and G. Pajares. Computers and Electronics in Agriculture, 75(2):337–346, 2011

Analysis of natural images processing for the extraction of agricultural elements. X. P. Burgos-Artizzu, A. Ribeiro, A. Tellaeche, G. Pajares, and C. Fernández-Quintanilla. Image and Vision Computing, 28(1):138–149, 2010

Improving weed pressure assessment using digital images from an experience-based reasoning approach. X. P. Burgos-Artizzu, Ribeiro, A. Tellaeche, G. Pajares, and C. Fernández-Quintanilla. Computers and Electronics in Agriculture, 65(2):176–185, 2009

A vision-based method for weeds identification through the bayesian decision theory. A. Tellaeche, X. P. Burgos-Artizzu, G. Pajares, and A. Ribeiro. Pattern Recognition, 41:521–530, 2008


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