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

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 Processing and Analysis /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.