Deep Learning for Medical Image Analysis

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Credits
3
Types
Elective
Requirements
This subject has not requirements
Department
UB
Mail
Medical imaging has evolved in the last few decades integrating new tools for the automatic diagnosis and clinical-decision support. Computer Vision and Deep learning tools are used in several image modalities (MR, CT, US, Dermatology) in order to provide diagnosis results comparable with clinical experts. This course will focus on specific methods for medical image analysis, pathology detection, data augmentation, image collection and preparation as well as how to generate and communicate meaningful insight from analytics.

Teachers

Person in charge

  • Simone Balocco ( )

Others

  • Oliver Díaz Montesdeoca ( )

Weekly hours

Theory
1
Problems
0
Laboratory
1
Guided learning
0
Autonomous learning
3.8

Competences

Generic Technical Competences

Generic

  • CG2 - Capability to lead, plan and supervise multidisciplinary teams.

Technical Competences of each Specialization

Academic

  • CEA3 - Capability to understand the basic operation principles of Machine Learning main techniques, and to know how to use on the environment of an intelligent system or service.
  • CEA4 - Capability to understand the basic operation principles of Computational Intelligence main techniques, and to know how to use in the environment of an intelligent system or service.
  • CEA6 - Capability to understand the basic operation principles of Computational Vision main techniques, and to know how to use in the environment of an intelligent system or service.
  • CEA8 - Capability to research in new techniques, methodologies, architectures, services or systems in the area of ??Artificial Intelligence.
  • CEA13 - Capability to understand advanced techniques of Modeling , Reasoning and Problem Solving, and to know how to design, implement and apply these techniques in the development of intelligent applications, services or systems.
  • CEA14 - Capability to understand the advanced techniques of Vision, Perception and Robotics, and to know how to design, implement and apply these techniques in the development of intelligent applications, services or systems.

Professional

  • CEP3 - Capacity for applying Artificial Intelligence techniques in technological and industrial environments to improve quality and productivity.
  • CEP6 - Capability to assimilate and integrate the changing economic, social and technological environment to the objectives and procedures of informatic work in intelligent systems.
  • CEP8 - Capability to respect the surrounding environment and design and develop sustainable intelligent systems.

Transversal Competences

Appropiate attitude towards work

  • CT5 - Capability to be motivated for professional development, to meet new challenges and for continuous improvement. Capability to work in situations with lack of information.

Basic

  • CB7 - Ability to integrate knowledges and handle the complexity of making judgments based on information which, being incomplete or limited, includes considerations on social and ethical responsibilities linked to the application of their knowledge and judgments.

Objectives

  1. Introduction to clinical imaging modalities.
    Data analysis techniques.
    Neural network for medical imaging
    Databases and challenges
    Related competences: CB7, CT5, CEA13, CEA3, CEA4, CEA6, CEA8, CEP8, CEA14, CEP3, CEP6, CG2,

Contents

  1. Introduction to the clinical image modalities
    Introduction to the clinical image modalities
  2. Techniques for data analysis
    Techniques for data analysis
  3. Neural network for medical imaging
    Neural network for medical imaging
  4. Data bases and challenges
    Data bases and challenges

Activities

Activity Evaluation act


Theory

Theory
Objectives: 1
Theory
12h
Problems
0h
Laboratory
0h
Guided learning
0h
Autonomous learning
13h

practicum

practicum
Objectives: 1
Theory
0h
Problems
0h
Laboratory
12h
Guided learning
0h
Autonomous learning
33h

Student presentations

Student presentations
Objectives: 1
Theory
1h
Problems
0h
Laboratory
1h
Guided learning
0h
Autonomous learning
3h

Teaching methodology

T – Each week it will be a 1h theoretical topic exposition class.
P – Each week it will be a 1h practical session.
The rest of the course are devoted to autonomous lectures, programming, and studying.

Evaluation methodology

The course will follow a continuous evaluation consisting in practical reports (PR) and in-class presentations (PS). A test (or multiple mini-tests) about the theory will be performed (TS). The final score (FS) will be computed as follows:
FS = 0.4 * PR + 0.3 * PS + 0.3 * TS
A minimum score of 3 over 10 points is required for each part PR, PS, and TS in order to compute the final score FS.

Bibliography

Basic:

  • A survey on deep learning in medical image analysis - Litjens, G., Kooi, T., Bejnordi, B. E., Setio, A. A. A., Ciompi, F., Ghafoorian, M., ... & Sánchez, C. I. , Medical image analysis, 42, 60-88. (2017). ISBN: https://doi.org/10.1016/j.media.2017.07.005
  • Guest editorial deep learning in medical imaging: Overview and future promise of an exciting new technique - Greenspan, H., Van Ginneken, B., & Summers, R. M. , IEEE Transactions on Medical Imaging, (2016) 35(5), 1153-1159.

Addendum

Teaching methodology

As far as possible, the gender perspective will be incorporated in the development of the subject. In addition, teachers will be attentive to those specific gender needs that students may raise, such as being able to choose a partner of the same gender if group work is carried out or being able to pose challenges against the gender gap.

Contingency plan

Teaching will follow a face-to-face (in-person), virtual (online) or mixed model according to the instructions of the competent authorities. In principle, we expect to follow the mixed teaching model for the 2021-22 academic year. * In case of in-person teaching: The weekly schedule of in-person activities is distributed in three hours of theory class that includes practice. * In case of mixed teaching required by the health situation (this is the expected model): If the health situation allows it and the necessary conditions are met, we expect to have between 50% and 70% of in-person activities. In general, when having an occupancy rate of 50%, students will attend in-person for a week and will follow class on streaming for the following week. For virtual teaching, material will be delivered so that students can consult it asynchronously. For in-person teaching, the time will be devoted to Questions and Answer sessions regarding the theory material or regarding the practical exercises. Priority will also be given to carrying out evaluation activities in person. Moreover, synchronous online sessions will be scheduled to keep the proper subject dynamics and / or the resolution of doubts that may arise (and that complement the raised in in-person activities). * In case on-line teaching is required by the health situation: The time ranges of mixed teaching are maintained but all teaching will be carried out in an online format.