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R Vega, P Gorji, Z Zhang, X Qin, A Rakkunedeth, J Kapur, J Jaremko, R Greiner. Sample Efficient Learning of Image-Based Diagnostic Classifiers Using Probabilistic Labels. Proceedings of the 24th International Conference on Artificial Intelligence and Statistics (AISTATS) 2021, San Diego, California, USA. PMLR: Volume 130. April 2021 (To be published) [preprint version]
D Gomez, C Power, MJ Gill, N Koenig, R Vega, E Fujiwara. Empiric neurocognitive performance profile discovery and interpretation in HIV infection. Journal of NeuroVirology. December 2018 [Journal version]
R Vega, R Greiner. Finding Effective Ways to (Machine) Learn fMRI-based Classifiers from
Multi-Site Data. Understanding and Interpreting Machine Learning in Medical Image Computing Applications. (1st International Workshop on Machine Learning in Clinical Neuroimaging: Spatially Structured Data Analysis. In Conjunction with MICCAI 2018). [Conference version][preprint version]
S Liang, R Vega, X Kong, W Deng, Q Wang, X Ma, M Li, X Hu, AJ Greenshaw, R Greiner, T Li. Neurocognitive graphs of first-episode schizophrenia and major depression based on cognitive features. Neuroscience bulletin 34 (2). 312-320. 2018 [preprint version] [Journal version]
J Guinney, T Wang, et al. Prediction of overall survival for patients with metastatic castration-resistant prostate cancer: development of a prognostic model through a crowdsourced challenge with open clinical trial data. The Lancet Oncology 18 (1). 132-142. 2017 [preprint version] [Journal version]
F Seyednasrollah, et al. A DREAM challenge to build prediction models for short-term discontinuation of docetaxel in metastatic castration-resistant prostate cancer patients. JCO Clinical Cancer Informatics 1, 1-15. 2017. [preprint version] [Journal version]
D Pinzon, R Vega, YP Sanchez, B Zheng. Skill learning from kinesthetic feedback. The American Journal of Surgery 214 (4), 721-725. 2017 [preprint version] [Journal version]
R Vega, T Sajed, KW Mathewson, K Khare, PM Pilarski, R Greiner, G Sanchez-Ante, JM Antelis. Assessment of feature selection and classification methods for recognizing motor imagery tasks from electroencephalographic signals. Artificial Intelligence Research 6 (1). 37. 2016 [Open Access]
R Vega, AG Hernandez-Reynoso, EK Linn, RQ Fuentes-Aguilar, G Sanchez-Ante, A Santos-Garcia, A Garcia-Gonzalez. Hemodynamic pattern recognition during deception process using functional infra-red spectroscopy. Journal of medical and biological engineering 36 (1), 22-31. 2016 [Open Access]
R Ocampo-Vega, G Sanchez-Ante, MA de Luna, R Vega, LE Falcon-Morales, H Sossa. Improving pattern classification of DNA microarray data by using PCA and logistic regression. Intelligent Data Analysis 20 (s1), S53-S67. 2016
R Vega. The challenge of applying machine learning techniques to diagnose schizophrenia using multi-site fMRI data. MSc thesis. University of Alberta. 2016 [Full thesis]
R Vega, G Sanchez-Ante, LE Falcon-Morales, H Sossa, E Guevara. Retinal Vessel extraction using lattice neural networks with dendritic processing. Computers in biology and medicine 58, 20-30. 2015 [preprint version] [Journal version]
L Ojeda, R Vega, LE Falcon, G Sancehz-Ante, H Sossa, JM Antelis. Classification of han movements from non-invasive brain signals using lattice neural networks with dendritic processing. Mexican Conference on Pattern Recognition, 23-32. 2015 [preprint version]
R Ocampo, MA de Luna, R Vega, G Sanchez-Ante, G Sanchez-Ante, LE Falcon-Morales, H Sossa. Pattern analysis in DNA microarray data through PCA-based gene selection. Iberoamerican Congress on Pattern Recognition, 532-539. 2014 [Conference version]
R Vega, E Guevara, LE Falcon, G Sanchez-Ante, H Sossa. Blood vessel segmentation in retinal images using lattice neural networks. Mexican International Conference on Artificial Intelligence, 532-544. 2013 [preprint version]