- Course
- 2021-2022
- Semester
- 2
- ECTS
- 6
- Type
- Elective
- University
- UDC and UPorto
Subject objectives
-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.
Contents
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 and complementary bibliography
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.
Competencies
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 to
computer 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
Teaching methodology
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
See Contingency Plan for alternative scenarios.
Evaluation system
-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
See Contingency Plan for alternative scenarios.
Studying time and personal work
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.
Subject study recommendations
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
Observations
Contingency plan:
1. Modifications to the contents None. 2. Methodologies *Teaching methodologies that
are maintained All of them. *Teaching methodologies that are modified If necessary, all
the used methodologies could be applied on a non-presential basis with the available tools
(Moodle, Teams, etc.)
3. Mechanisms for personalized attention to students Continuous
attention in Teams, Moodle and email.
4. Modifications in the evaluation Not necessary.
*Evaluation observations: None.
5. Modifications to the bibliography or webgraphy None.