SE3.1) Estructurar datos a partir de fuentes multimodales y no estructuradas para inferir nuevo conocimiento SE3.4) Desarrollar herramientas de asistencia al diagnóstico y toma de decisión en salud SE3.2) Evaluar algoritmos de análisis de imagen destinados a solucionar problemas específicos de salud
Activity type Hours with a teacher Hours without a teacher Virtual hours with a teacher Total Analysis / case study 0 40,00 10,00 50,00 Participatory class 0 20,00 5,00 25,00 Total 0 60,00 15,00 75
González, Rafael C. (2004). Digital image processing using Matlab. Upper Saddle River: Prentice Hall. Catàleg Friston, K. J. (Karl J.) (2007). Statistical parametric mapping : the analysis of functional brain images (1st ed.). London: Academic Catàleg M. Jenkinson, C.F. Beckmann, T.E. Behrens, M.W. Woolrich, S.M. Smith (2012). FSL. NeuroImage, 62(2), Mark Jenkinson, Michael Chappell (2021). Introduction to Neuroimaging Analysis. Oxford University Press. Bielza, Concha (2021). Data-driven computational neuroscience: machine learning and statistical models. Cambridge, UK ;: Cambridge University Press. Catàleg Wallisch, Pascal. Lusignan, Michael E.. Benayoun, Marc D.. Baker, Tanya, I.. Dickey, Adam S.. Hatsopoulos, Nicholas G. (2014). MATLAB for neuroscientists : an introduction to scientific computing in MATLAB (Second edition). Amsterdam: Academic. Catàleg King, Andrew P. (2017). MATLAB programming for biomedical engineers and scientists. Amsterdam: Academic Press. Catàleg Subasi, Abdulhamit (2019). Practical guide for biomedical signals analysis using machine learning techniques : a MATLAB® based approach. London: Elsevier/Academic Press. Catàleg
Assessment activities: Description of the activity Assessment Activity % Remediable subject Problem with practical component about EEG signal processing Evaluation criteria will be provided with the problem description. 50 Yes Problem with practical component about image analysis on neuroimages. Evaluation criteria will be provided with the problem description. 50 Yes
The final mark of the subject will be calculated according to the weights of each of the proposed activities. Specific criteria for the "No show" grade: NP will be considered when the students do not submit any of the evaluation activities. Single Assessment: This is an interuniversity program, that does not consider this kind of evaluation. Minimum requirements to pass: The minimum qualification to pass the course is 5.0
To stablish the apointments, students can user or sent mails to the professors. These appointments can be done online via googlemeet / zoom / TAEMS metting.
The communication and interaction with the students will be mainly done via moodle, having also specific forums for the activities. Students can also interact with the professors via email or via videoconferences (googlemeet, zoom, TEAMS).
Updated information in the Moodle of the Master site https://www.urv.cat/en/studies/master/courses/health-data-science/