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This course will help a student acquire knowledge, skills and insight in machine learning in the domain of medical imaging and sensor data. The course will begin with an introduction to the classical techniques in classification, supervised and unsupervised learning, and regression. This will be followed by the newly developed field of machine learning. While the concepts are general, the focus will be on the medical applications of these concepts.


The course addresses the machine learning and deep learning paradigm for classification and pattern recognition in vast amounts of data. This course is an introduction to classical machine learning and the new paradigms such as convolutional neural nets for deep learning. The topics that will be covered include:

General Information

For Whom

PhD students, young scientists and UT students with a background in Biomedical Technology, Technical Medicine, and related fields. Primarily for students with a need for more insight in machine learning with application to medical image data.


7 - 12 June 2021


5 full days



  • The price for this five-day course is € 550,00 
  • Attending the course is free of charge for UT master students
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UT Master students enrolled in the course '3D Computer Vision for Medical Applications' are enrolled automatically and should not use this button
  • Programme

    Each day of this 5-day course is organized as follows:

    • Introduction to the theoretical topics of the day
    • Clinical context and clinical relevance of the topic covered
    • Matlab hands-on exercise illustrating the theoretical concepts with clinical examples
    • Re-cap, closing remarks, and closure of the day
    • Assessment/Examination for the day
    • Day 1: Introduction to Machine Learning paradigms and their significance to medicine.

      Two basic classification algorithms will be outlined. Hands-on experience will be offered in which these classifiers are trained with data from a relevant clinical problem.

    • Day 2: Learning strategies for more advanced classification algorithms.

      More advanced classifiers will be introduced, such as the Support Vector Machine (SVM), and the feedforward neural network with backpropagation. The pitfalls which might occur during the training of these classifiers will be elucidated with a clinical example. In this exercise, also strategies for countermeasures will be demonstrated.

    • Day 3: Feature generation and reduction.

      Medical images are 3D data volumes that carry rich information about anatomical and functional structures and possible diseases. However, the 3D image data are voluminous, low-level representations of information. Feature generation, such as in radiomics, reduces the data size by grasping the local statistics of the image in so-called features. Techniques for a further, application dependent reduction of number of features is the topic of day 3.

    • Day 4 and 5: Deep Learning in Medicine

      Deep learning networks, and specifically the convolutional neural network, will be introduced. Their clinical significance, based on a historical overview, will be presented, together with the implementation aspects. Participants will be offered hands-on training with a simple convolutional neural network.

  • Additional Information

    The course is organized by:

    • Dr Ferdinand (Ferdi) van der Heijden, PhD – University of Twente
    • Prof Rajiv (Raj) Gupta, MD, PhD – Massachusetts General Hospital and Harvard Med School
    • Can (John) O. Tan, PhD – Spaulding Rehab Hospital, MGH and Harvard University
    • Frieda Limbeek-van den Noort, MSc – University of Twente
    • under the auspices of the Technical Medicine department of the University of Twente in collaboration with the Medical Imaging group of the MIRA Institute for Biomedical Technology and Technical Medicine.

    The course is part of the regular UT curriculum and welcomes attendance from external participants. 

    The course assumes a background in programming. In addition to the medical relevance of the topics being covered, the exposition to the theoretical concepts will be made more concrete by a set of well-selected programming exercises. The students will be expected to program extensively by themselves. This will enable them to quickly apply their newfound knowledge and learn the material from a practical standpoint.

    Familiarity and experience with Matlab and with vector-matrix calculus is a prerequisite for this course.


    • The course will represent 1.5 EC
    • This ML course is part of the optional course “3D Computer Vision for Medical Applications” (5 EC). If you enrol yourself for that course in Osiris, then you are automatically also enrolled for this ML course. Do NOT use the enrolment link above.


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