Medical Image Computing

  • Master's program Medical Technologies
Kennzahl der Lehrveranstaltung
  • MSE-M-3-ILV-IM2
Niveau der Lehrveranstaltung laut Lehrplan
  • Master
Semester in dem die Lehrveranstaltung angeboten wird
  • 3
Anzahl der zugewiesenen ECTS-Credits
  • 5.0
Name des/der Vortragenden
  • Dipl.-Ing. Augustin Marco, PhD
Lernergebnisse der Lehrveranstaltung
  • Students
    • Understand scientific principles of the major medical imaging modalities
    • Extend the knowledge and skills required for a career in an image-related field in clinical practice, clinical research, scientific research or technical development.
    • Know about applied big data in health care
    • Know methods for pattern recognition and classification
    • Are able to apply these methods on medical images using MATLAB
    • Know the basics of unsupervised machine learning for medical images
    • Know tools for therapy planning using medical images
Art der Veranstaltung
  • face-to-face
Voraussetzungen laut Lehrplan
  • none
  • - Medical Imaging Modalities
    - Segmentation, image processing
    - Pattern recognition in medical imaging
    - Linear classification
    - Nonlinear classification
    - Unsupervised machine learning from medical images
    - Image guided therapy planning
empfohlene Fachliteratur
  • - P. Suetens, Fundamentals of medical imaging, 3rd edition, Cambridge: Cambridge University Press, 2017.
    - R. C. Gonzalez, R. E. Woods, Digital Image Processing, Global Edition (Englisch), 4th edition, Pearson, 2017
    - O. S. Pianykh, Digital Imaging and Communications in Medicine (DICOM), 2nd edition, Springer, 2012
    - A. P. Dhawan, Medical Image Analysis, 2nd edition, Wiley-IEEE Press, 2011
    - A. Géron, Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow, 2nd edition, O'Reilly Media, 2019
Lehr- und Lernformen
  • The course comprises an interactive mix of lectures, discussions and individual and group work.
  • To monitor the students’ learning this course will provide ongoing assignments as a basis for feedback and grading (formative assessment) and/or will evaluate the students learning at the end of the course or an instructional unit via exams, final project reports, essays or seminar papers (summative assessment).
  • English