ECTS credits ECTS credits: 6
ECTS Hours Rules/Memories Student's work ECTS: 109 Hours of tutorials: 1 Expository Class: 14 Interactive Classroom: 26 Total: 150
Use languages English
Type: Ordinary subject Master’s Degree RD 1393/2007 - 822/2021
Departments: External department linked to the degrees
Areas: Área externa Máster en Visión por Computador / Computer Vision
Center Higher Technical Engineering School
Call: Second Semester
Teaching: With teaching
Enrolment: Enrollable | 1st year (Yes)
-Knowledge of specific advanced techniques for biomedical image processing
and analysis.
-Analysis of current biomedical imaging applications, and ability to evaluate
existing solutions, as well as the development of new specific solutions
- Evaluation of the adequacy of applied methodologies in a multidisciplinary
context for biomedical environments.
-Ability to write documentation and reports on scientific and technical results.
Advanced biomedical image processing and analysis techniques
Advanced segmentation techniques in biomedical imaging
Pattern recognition in biomedical imaging
Advanced brain imaging techniques
Advanced biomedical image analysis applications
Basic
Handbook of Biomedical Image Analysis (Editors: Wilson, David, Laxminarayan, Swamy). 2005
Aly A. Farag, Biomedical Image Analysis, Statistical and Variational Methods. 2014
Articles in conferences and journals of the area (ISBI, MICCAI, T-MI, IEEE Transactions on Biomedical Engineering, etc.)Recommended study time for students is about 2 hours per week. Additionally, we estimate that they should spend about 6,5 hours / week working in a number of assignments. All of these activities add up to around 120h/semester.
A1 CE1 – To know and apply the concepts, methodologies and technologies of image processing
A2 CE2 – To know and apply machine learning and pattern recognition techniques applied tocomputer vision
A5 CE5 – To analyze and apply methods of the state of the art in computer vision
A7 CE7 – To understand and apply the fundamentals of medical image acquisition, processing and analysis
A8 CE8 – To communicate and disseminate the results and conclusions of research in the field of computer vision Study programme competences: Basic / General
B1 CB6 – To possess and understand knowledge that provides a basis or opportunity to be
original in the development and/or application of ideas, often in a research context
B3 CB8 – That students are able to integrate knowledge and deal with the complexity of making
judgements based on information that is incomplete or limited, including reflections on social
and ethical responsibilities linked to the application of their knowledge and judgements
B7 CG2 – Ability to analyze a company’s needs in the field of computer vision and determine the
best technological solution for itB10 CG5 – Ability to identify unsolved problems and provide innovative solutions
B11 CG6 – Ability to identify theoretical results or new technologies with innovative potential and
convert them into products and services useful to society
Study programme competences: Transversal / Nuclear
C3 CT3 – Development of the innovative and entrepreneurial spirit
1st Part:
Laboratory practice:
Practice in computer classrooms, learning based on the resolution of practical cases, combining work and autonomous learning with group work for cooperative learning
Guest lecture / keynote:
speech Participatory Master Lessons
Supervised projects:
Presentations of project-oriented works
2nd Part:
Journal Club: Student(s) prepare and present a paper content to the class.
Thematic Review monography (individual or group).
Hands-on Project with report (individual or group).
-Laboratory practice (50):
Competences: A5 A8 B3 B10
Development practices of applied cases
-Supervised projects (30):
Competences:A5 A8 B3 B10
Practical projects related to the subject
-Guest lecture / keynote speech (20)
Competences: A1 A2 A7 B1
B7 B11 C3
Demonstration of application of knowledge taught in class
For the second part, specifically: Practical projects related to the subject (mandatory presence >75% of classes).
All components may be subjected to oral exam / discussion (e.g. Top 5% grade decision).
Recommended study time for students is about 2 hours per week. Additionally, we estimate that they should spend about 6,5 hours / week working in a number of assignments. All of these activities add up to around 120h/semester.
Subjects that it is recommended to have taken before:
Fundamentals of Machine Learning for Computer Vision /614535007
Instrumentation and Processing for Machine Vision/614535009
Fundamentals of Image Analysis and Processing/614535001
For the second part: Minimal grade is 7/20 (35%) per component. All components together must grade 9,5/20 (47,5%) for pass.