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Cursus: 202100259
202100259
3D Medical Lab
Cursus informatie
Cursus202100259
Studiepunten (ECTS)5
CursustypeCursus
VoertaalNederlands
ContactpersoonC.O. Tan
E-mailc.o.tan@utwente.nl
Docenten
Contactpersoon van de cursus
C.O. Tan
Examinator
C.O. Tan
Collegejaar2021
Aanvangsblok
2B
Aanmeldingsprocedure-
Inschrijven via OSIRISJa
Cursusdoelen
After completing this course, students will be able to:
 
  1. Recognize clinical issues that can be solved effectively and efficiently with a 3D technology approach, and acquire, create,and manipulate 3D mesh data. They will also be able to understand the importance and role of quantitative image analysis in clinical research and practice. This will include getting familiar and proficient with open source medical image exploration tools.
 
  1. Recognize clinical issues and problems that can be solved effectively and efficiently with a machine learning approach. They will also be able to identify and understand the utility of machine learning in diagnosis and in implementation of personalized medicine, and to fluently and effectively select appropriate tools and interpret their outcome
 
  1. Understand the fundamentals of deep learning, its relevance to machine learning, and how it can be used in lieu of other open source tools (above) for 3D image processing, registration, and interpretation
 
  1. Specify functional and structural architecture and basic design to implements deep learning models. They will be able to implement and/or extend appropriate deep learning architectures using python
 
  1. Identify, understand, and quantify the limitations of deep learning architectures, implementations, and application
Inhoud
Broadly, this course seeks to provide a treatment of 3D technology, machine learning and/or deep learning technologies. Students will learn to implement computer vision, machine learning, and deep learning approaches to scientific research, clinical practice, clinical diagnosis, and individualized medicine. They will also learn, practice, administer and/or utilize these technologies in their clinical practice and research.

The emphasis of this course is on understanding of the principles underlying these technologies, and developing insights to their limitations for clinical applications. A full, rigorous engineering or mathematical treatment of these technologies is beyond the scope of this course.

This course is open only to TM students.

Visiting professor (Massachusetts General Hospital and Harvard Med School): Gupta, R..
Gastdocent (MST): Doremale, Rob van
Gastdocent (RUMCN): Maal, T.J.J.
Voorkennis
Mandatory
Segmentation and Visualization (201200168-2A)
Participating study
Master Technical Medicine
Verplicht materiaal
Course material
Syllabi and lecture sheets, covering the subject matter, will be made available via Canvas
Course material
Work books: Pdf forms will be used for exercises
Course material
Python environment (will be covered in one of the earlier session), and MATLAB 2019a or higher. Required toolboxes for MATLAB: Computer Vision System Toolbox, Image Processing Toolbox, Neural Network Toolbox, Statistics and Machine Learning Toolbox
Aanbevolen materiaal
-
Werkvormen
Hoorcollege

Practicum
AanwezigheidsplichtJa

Zelfstudie met begeleiding
AanwezigheidsplichtJa

Toetsen
Test

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