Identifying Data 2020/21
Subject (*) Biomedical Image Analysis Code 614535013
Study programme
Máster Universitario en Visión por Computador
Descriptors Cycle Period Year Type Credits
Official Master's Degree 2nd four-month period
First Optional 6
Language
English
Teaching method Hybrid
Prerequisites
Department Ciencias da Computación e Tecnoloxías da Información
Coordinador
Novo Bujan, Jorge
E-mail
j.novo@udc.es
Lecturers
Novo Bujan, Jorge
Ortega Hortas, Marcos
E-mail
j.novo@udc.es
m.ortega@udc.es
Web
General description
Contingency plan 1. Modificacións nos contidos
Ningunha.

2. Metodoloxías
*Metodoloxías docentes que se manteñen
Todas.

*Metodoloxías docentes que se modifican
En caso de necesidade, todas as metodoloxías empregadas poderían aplicarse de modo non presencial coas ferramentas dispoñibles (Moodle, Teams, etc.)

3. Mecanismos de atención personalizada ao alumnado
Atención continuada en Teams, Moodle e correo electrónico.

4. Modificacións na avaliación
Non son necesarias.

*Observacións de avaliación:
Ningunha.

5. Modificacións da bibliografía ou webgrafía
Ningunha.

Study programme competencies
Code Study programme competences
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
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 it
B10 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
C3 CT3 - Development of the innovative and entrepreneurial spirit

Learning aims
Learning outcomes Study programme competences
Knowledge of specific advanced techniques for biomedical image processing and analysis. AC1
AC2
AC5
AC7
AC8
BC1
BC3
BC7
BC10
BC11
CC3
Analysis of current biomedical imaging applications, and ability to evaluate existing solutions, as well as the development of new specific solutions AC1
AC2
AC5
AC7
AC8
BC1
BC3
BC7
BC10
BC11
CC3
Evaluation of the adequacy of applied methodologies in a multidisciplinary context for biomedical environments. AC1
AC2
AC5
AC7
AC8
BC1
BC3
BC7
BC10
BC11
CC3
Ability to write documentation and reports on scientific and technical results. AC1
AC2
AC5
AC7
AC8
BC1
BC3
BC7
BC10
BC11
CC3

Contents
Topic Sub-topic
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

Planning
Methodologies / tests Competencies Ordinary class hours Student’s personal work hours Total hours
Laboratory practice A5 A8 B3 B10 15 51.84 66.84
Guest lecture / keynote speech A1 A2 A7 B1 B7 B11 C3 14 21.6 35.6
Supervised projects A5 A8 B3 B10 10 34.56 44.56
 
Personalized attention 3 0 3
 
(*)The information in the planning table is for guidance only and does not take into account the heterogeneity of the students.

Methodologies
Methodologies Description
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

Personalized attention
Methodologies
Laboratory practice
Supervised projects
Description

Attention to the challenges that the students are exposed to both in the practices and in the works exhibited.

Assessment
Methodologies Competencies Description Qualification
Laboratory practice A5 A8 B3 B10 Development practices of applied cases 50
Supervised projects A5 A8 B3 B10 Practical projects related to the subject 30
Guest lecture / keynote speech A1 A2 A7 B1 B7 B11 C3 Demonstration of application of knowledge taught in class 20
 
Assessment comments

Sources of information
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.)

Complementary


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

Subjects that are recommended to be taken simultaneously

Subjects that continue the syllabus

Other comments


(*)The teaching guide is the document in which the URV publishes the information about all its courses. It is a public document and cannot be modified. Only in exceptional cases can it be revised by the competent agent or duly revised so that it is in line with current legislation.