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- Artículo científico. Roman Koposov, Thomas Frodl, Øystein Nytrø, Bennett Leventhal, Andre Sourander, Silvana Quaglini, Massimo Molteni, María de la Iglesia Vayá, Hans Ulrich Prokosch, Nicola Barbarini, Michael Peter Milham, Francisco Xavier Castellanos and Norbert Skokauskas. 2017. Clinical Decision Support Systems for Child Neuropsychiatric Disorders: The Time Has Come? Ann Cogn Sci 1(1):12-15 DOI: 10.36959/447/335
- Artículo científico. Roman Koposov, Sturla Fossum, Thomas Frodl, Øystein Nytrø, Bennett Leventhal, Andre Sourander, Silvana Quaglini, Mass Molteni, María de la Iglesia Vayá, Hans-Ulrich Prokosch, Nicola Barbarini, Michael Peter Milham, Francisco Xavier Castellanos & Norbert Skokauskas. 2017. Clinical decision support systems in child and adolescent psychiatry: a systematic review. European Child & Adolescent Psychiatry. 26, 1309-1317 (2017). https://doi.org/10.1007/s00787-017-0992-0
- Artículo científico. Molla B, Munoz-Lasso DC, Riveiro F, Bolinches-Amoros A, Pallardo FV, Fernandez-Vilata A, de la Iglesia-Vaya M, Palau F and Gonzalez-Cabo P. 2017. Reversible Axonal Dystrophy by Calcium Modulation in Frataxin-Deficient Sensory Neurons of YG8R Mice.. Frontiers in Molecular Neuroscience 2017; 10: art 264. Available from: www.doi.org/10.3389/fnmol.2017.00264
- Artículo científico. Rojas, Gonzalo M.; Alvarez, Carolina; Montoya, Carlos E.; María de la Iglesia-Vayá; Jaime E Cisternas and Marcelo Gálvez 2018. Study of Resting-State Functional Connectivity Networks Using EEG Electrodes Position As Seed. Frontiers in Neuroscience. 12, pp.235-235. ISSN 1662-453X. https://doi.org/10.3389/fnins.2018.00235
- Revista. María de la Iglesia-Vayá, Jose María Salinas, Rosa Llopis Penadés, Cayetano Hernández Marín, Rosario Rodríguez López, Pablo Sánchez Manchón, Amparo García Medina, , Carlos Muñoz Núñez , Marisa Caparrós Redondo, Ana María Ávila Peñalver, Carmen Ferrer Ripolles Imagen Médica Poblacional como Impulsora de la Transformación Digital en los Sistemas de Información para la Salud de la CSUSP Revista de la sociedad española de informática y salud (I+S), núm. 132. Diciembre 2018
- Artículo científico. M. Magdalena Sepúlveda, Gonzalo M.Rojas, Evelyng Faure, Claudio R.Pardo, Facundo las Heras; Cecilia Okuma, Jorge Cordovez, María de la Iglesia-Vayá, José Molina-Mateo, Marcelo Gálvez. 2019. Visual Analysis of automated segmentation in the diagnosis of focal cortical dysplasias with magnetic resonance imaging Epilepsy & Behavior. elsevier. 12, pp.235-235. ISSN 1662-453X. https://doi.org/10.1016/j.yebeh.2019.106684
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Germán González, Aurelia Bustos, José María Salinas, María de la Iglesia-Vaya, Joaquín Galant, Carlos Cano-Espinosa, Xavier Barber, Domingo Orozco-Beltrán, Miguel Cazorla, Antonio Pertusa. 2020. UMLS-ChestNet: A deep convolutional neural network for radiological findings, differential diagnoses and localizations of COVID-19 in chest x-rays ArXiv.4
Irene Pérez-Díez, Raúl Pérez-Moraga, Adolfo López-Cerdán, Marisa Caparrós Redondo, Jose-Maria Salinas-Serrano, María de la Iglesia-Vayá De-identifying Spanish medical texts - named entity recognition applied to radiology reports. J Biomed Semant 12, 6 (2021). https://doi.org/10.1186/s13326-021-00236-2 - Artículo científico. 1; 2; 3; et al. 2012. Segmentación automática del cerebro: un nuevo enfoque en imágenes de RM potenciadas en T1 Radiología. ISSN 0033-8338. 44, pp.240-241.
José F. Català-Senent, Marta R. Hidalgo, Marina Berenguer, Gopanandan Parthasarathy, Harmeet Malhi, Pablo Malmierca-Merlo, María de la Iglesia-Vayá & Francisco García-García Hepatic steatosis and steatohepatitis: a functional meta-analysis of sex-based differences in transcriptomic studies. Biol Sex Differ 12, 29 (2021). https://doi.org/10.1186/s13293-021-00368-1
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Bannier E, Barker G, Borghesani V, Broeckx N, Clement P, Emblem KE, Ghosh S, Glerean E, Gorgolewski KJ, Havu M, Halchenko YO, Herholz P, Hespel A, Heunis S, Hu Y, Hu CP, Huijser D, de la Iglesia Vayá M, Jancalek R, Katsaros VK, Kieseler ML, Maumet C, Moreau CA, Mutsaerts HJ, Oostenveld R, Ozturk-Isik E, Pascual Leone Espinosa N, Pellman J, Pernet CR, Pizzini FB, Trbalić AŠ, Toussaint PJ, Visconti di Oleggio Castello M, Wang F, Wang C, Zhu H. The Open Brain Consent: Informing research participants and obtaining consent to share brain imaging data. Hum Brain Mapp. 2021 May;42(7):1945-1951. doi: 10.1002/hbm.25351. Epub 2021 Feb 1. PMID: 33522661; PMCID: PMC8046140.
Aurelia Bustos, Antonio Pertusa, Jose-Maria Salinas, Maria de la Iglesia-Vayá,PadChest: A large chest x-ray image dataset with multi-label annotated reports,
Medical Image Analysis, Volume 66, 2020, 101797, ISSN 1361-8415, https://doi.org/10.1016/j.media.2020.101797.
(https://www.sciencedirect.com/science/article/pii/S1361841520301614)
Martí Bonmatí, Luis ;Valenzuela Juan, Rosa ;de la Iglesia Vayá, Maria. Where is a very important part of the factual information about our services?. 2011. Imagen Diagnóstiva (Elsevier) Vol 2 pp 45-46. DOI: 10.1016/S2171-3669(11)70029-4
Daniel Arias-Garzón, Jesús Alejandro Alzate-Grisales, Simon Orozco-Arias, Harold Brayan Arteaga-Arteaga, Mario Alejandro Bravo-Ortiz, Alejandro Mora-Rubio, Jose Manuel Saborit-Torres, Joaquim Ángel Montell Serrano, Maria de la Iglesia Vayá, Oscar Cardona-Morales, Reinel Tabares-Soto,
COVID-19 detection in X-ray images using convolutional neural networks,
Machine Learning with Applications,
Volume 6,
2021,
100138,
ISSN 2666-8270,
https://doi.org/10.1016/j.mlwa.2021.100138.
(https://www.sciencedirect.com/science/article/pii/S2666827021000694)
Abstract: COVID-19 global pandemic affects health care and lifestyle worldwide, and its early detection is critical to control cases' spreading and mortality. The actual leader diagnosis test is the Reverse transcription Polymerase chain reaction (RT-PCR), result times and cost of these tests are high, so other fast and accessible diagnostic tools are needed. Inspired by recent research that correlates the presence of COVID-19 to findings in Chest X-ray images, this papers' approach uses existing deep learning models (VGG19 and U-Net) to process these images and classify them as positive or negative for COVID-19. The proposed system involves a preprocessing stage with lung segmentation, removing the surroundings which does not offer relevant information for the task and may produce biased results; after this initial stage comes the classification model trained under the transfer learning scheme; and finally, results analysis and interpretation via heat maps visualization. The best models achieved a detection accuracy of COVID-19 around 97%.
Keywords: COVID-19; Deep learning; Transfer learning; X-ray; Segmentation
Michael Milham, Chris Petkov, Pascal Belin, Suliann Ben Hamed, Henry Evrard, Damien Fair, Andrew Fox, Sean Froudist-Walsh, Takuya Hayashi, Sabine Kastner, Chris Klink, Piotr Majka, Rogier Mars, Adam Messinger, Colline Poirier, Charles Schroeder, Amir Shmuel, Afonso C. Silva, Wim Vanduffel, David C. Van Essen, Zheng Wang, Anna Wang Roe, Melanie Wilke, Ting Xu, Mohammad Hadi Aarabi, Ralph Adolphs, Aarit Ahuja, Ashkan Alvand, Celine Amiez, Joonas Autio, Reza Azadi, Eunha Baeg, Ruiliang Bai, Pinglei Bao, Michele Basso, Austin K. Behel, Yvonne Bennett, Boris Bernhardt, Bharat Biswal, Sethu Boopathy, Susann Boretius, Elena Borra, Rober Boshra, Elizabeth Buffalo, Long Cao, James Cavanaugh, Amiez Celine, Gianfranco Chavez, Li Min Chen, Xiaodong Chen, Luqi Cheng, Francois Chouinard-Decorte, Simon Clavagnier, Justine Cléry, Stan J. Colcombe, Bevil Conway, Melina Cordeau, Olivier Coulon, Yue Cui, Rakshit Dadarwal, Robert Dahnke, Theresa Desrochers, Li Deying, Kacie Dougherty, Hannah Doyle, Carly M. Drzewiecki, Marianne Duyck, Wasana Ediri Arachchi, Catherine Elorette, Abdelhadi Essamlali, Alan Evans, Alfonso Fajardo, Hector Figueroa, Alexandre Franco, Guilherme Freches, Steve Frey, Patrick Friedrich, Atsushi Fujimoto, Masaki Fukunaga, Maeva Gacoin, Guillermo Gallardo, Lixia Gao, Yang Gao, Danny Garside, Eduardo A. Garza-Villarreal, Maxime Gaudet-Trafit, Marzio Gerbella, Steven Giavasis, Daniel Glen, Ana Rita Ribeiro Gomes, Sandra Gonzalez Torrecilla, Alessandro Gozzi, Roberto Gulli, Suzanne Haber, Fadila Hadj-Bouziane, Satoka Hashimoto Fujimoto, Michael Hawrylycz, Quansheng He, Ye He, Katja Heuer, Bassem Hiba, Felix Hoffstaedter, Seok-Jun Hong, Yuki Hori, Yujie Hou, Amy Howard, Maria de la Iglesia-Vaya, Takuro Ikeda, Lucija Jankovic-Rapan, Jorge Jaramillo, Hank P. Jedema, Hecheng Jin, Minqing Jiang, Benjamin Jung, Igor Kagan, Itamar Kahn, Gregory Kiar, Yuki Kikuchi, Bjørg Kilavik, Nobuyuki Kimura, Ulysse Klatzmann, Sze Chai Kwok, Hsin-Yi Lai, Franck Lamberton, Julia Lehman, Pengcheng Li, Xinhui Li, Xinjian Li, Zhifeng Liang, Conor Liston, Roger Little, Cirong Liu, Ning Liu, Xiaojin Liu, Xinyu Liu, Haidong Lu, Kep Kee Loh, Christopher Madan, Loïc Magrou, Daniel Margulies, Froesel Mathilda, Sheyla Mejia, Yao Meng, Ravi Menon, David Meunier, A.J. Mitchell, Anna Mitchell, Aidan Murphy, Towela Mvula, Michael Ortiz-Rios, Diego Emanuel Ortuzar Martinez, Marco Pagani, Nicola Palomero-Gallagher, Vikas Pareek, Pierce Perkins, Fernanda Ponce, Mark Postans, Pierre Pouget, Meizhen Qian, Julian "Bene" Ramirez, Erika Raven, Isabel Restrepo, Samy Rima, Kathleen Rockland, Nadira Yusif Rodriguez, Elise Roger, Eduardo Rojas Hortelano, Marcello Rosa, Andrew Rossi, Peter Rudebeck, Brian Russ, Tomoko Sakai, Kadharbatcha S. Saleem, Jerome Sallet, Stephen Sawiak, David Schaeffer, Caspar M. Schwiedrzik, Jakob Seidlitz, Julien Sein, Jitendra Sharma, Kelly Shen, Wei-an Sheng, Neo Sunhang Shi, Won Mok Shim, Luciano Simone, Nikoloz Sirmpilatze, Virginie Sivan, Xiaowei Song, Aaron Tanenbaum, Jordy Tasserie, Paul Taylor, Xiaoguang Tian, Roberto Toro, Lucas Trambaiolli, Nick Upright, Julien Vezoli, Sam Vickery, Julio Villalon, Xiaojie Wang, Yufan Wang, Alison R. Weiss, Charlie Wilson, Ting-Yat Wong, Choong-Wan Woo, Bichan Wu, Du Xiao, Augix Guohua Xu, Dongrong Xu, Zhou Xufeng, Essa Yacoub, Ningrong Ye, Zhang Ying, Chihiro Yokoyama, Xiongjie Yu, Shasha Yue, Lu Yuheng, Xin Yumeng, Daniel Zaldivar, Shaomin Zhang, Yuguang Zhao, Zhanguang Zuo,
Toward next-generation primate neuroscience: A collaboration-based strategic plan for integrative neuroimaging,
Neuron,
2021, ISSN 0896-6273, https://doi.org/10.1016/j.neuron.2021.10.015.
(https://www.sciencedirect.com/science/article/pii/S0896627321007832)
Abstract: Open science initiatives are creating opportunities to increase research coordination and impact in nonhuman primate (NHP) imaging. The PRIMatE Data and Resource Exchange community recently developed a collaboration-based strategic plan to advance NHP imaging as an integrative approach for multiscale neuroscience.
The Open Brain Consent: Informing research participants and obtaining consent to share brain imaging data.
Bannier E, Barker G, Borghesani V, Broeckx N, Clement P, Emblem KE, Ghosh S, Glerean E, Gorgolewski KJ, Havu M, Halchenko YO, Herholz P, Hespel A, Heunis S, Hu Y, Hu CP, Huijser D, de la Iglesia Vayá M, Jancalek R, Katsaros VK, Kieseler ML, Maumet C, Moreau CA, Mutsaerts HJ, Oostenveld R, Ozturk-Isik E, Pascual Leone Espinosa N, Pellman J, Pernet CR, Pizzini FB, Trbalić AŠ, Toussaint PJ, Visconti di Oleggio Castello M, Wang F, Wang C, Zhu H.
Hum Brain Mapp. 2021 May;42(7):1945-1951. doi: 10.1002/hbm.25351. Epub 2021 Feb 1.
PMID: 33522661 Free PMC article - Artículo científico. Ignacio Blanquer; Miguel Caballer; Marti-Bonmatí, L.; María de la Iglesia Vayá et al. 2015. A Cloud Infrastructure for Scalable Computing on Population Imaging Databanks. International Journal of Image Mining, Vol. 1 Issue 2-3. https://doi.org/10.1504/IJIM.2015.073015