Deep Learning for Medical Image Analysis

Credits
3
Types
Elective
Requirements
This subject has not requirements , but it has got previous capacities
Department
UB
Mail
simone.balocco@ub.edu
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

Others

Weekly hours

Theory
1
Problems
0
Laboratory
1
Guided learning
0
Autonomous learning
3.76

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, Medical image analysis, 42, 60-88. (2017).
      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.
      https://ieeexplore.ieee.org/document/7463094

    Web links

    • for more information, please visit: https://www.ub.edu/pladocent/?cod_giga=575047&curs=2024&idioma=ENG http://Pla docent UB

    Previous capacities

    The previous knowledge required for this curse are:
    - Good understanding of basic concepts and methods of Deep Learning.
    The previous knowledge recommended for this curse are:
    - Familiarity with basic concepts and methods of Computer Vision.
    - good programming skills