Education

As an employee at a university I am quite involved in (arguably) our core business: educating talented scientists and engineers! This page provides an overview of my experience and involvement in this field.

 

Courses


Image Analysis for Health-care Technologies

  • I teach two modules on machine learning for which I also made the course material (covering: classification methods, ConvNets, Transfer learning and validation methods).

Advanced Video Content Analysis and Video Compression

  • Developed a module on Decision Forests and I present the lecture each time the course runs. 
  • I developed exercise sets and I grade the student reports on these exercises.

Introduction Medical Imaging Processing

  • Grade student reports on basic image processing methods (edge detection, contrast enhancement, filtering, etc.).
  • Developed and teach a module on classification, where I elaborate on three popular classification methods (kNN, SVM, Random Forests) with illustrating examples (code to generate these follows soon). 
  • Assist with lectures and instructions.
  • Developed 4 (out of 8) exercises for the written exam.

Spectrum of Electrical Engineering

  • Lecture on writing scientific publications for 1st-year students.
  • Available for interviews with students.

 

Supervised students


 

Arnaud Setio (4-month internship, MSc. EE)

  • Study on textural representation of early Barrett's cancer.
  • Work resulted in a publication on an international conference (VISAPP, 2013).

Arash Pourtaherian (4-month internship, MSc. EE)

  • Study on textural representation of early Barrett's cancer.

Louis Dercourt (6-month internship, MSc. EE)

  • Assignment to implement specific filters in C++.

Mark Janse (4-month internship, BSc. EE)

  • Study on the power of Random Forests for endoscopic image analysis.
  • Work resulted in a publication on an international conference (SPIE Medical Imaging, 2016).

Bart van Dongen (4-month internship, MSc. EE)

  • Study on the automatic quality assessment of endoscopic video frames.
  • Work resulted in a publication on an international conference (ISBI, 2016).

Sander Klomp (4-month internship, BSc. EE)

  • Study on machine learning methods for cancer detection in Volumetric Laser Endomicroscopy (VLE) images.
  • Work resulted in a publication on an international conference (SPIE Medical Imaging, 2017).
  • Graduated cum laude.

Jelle Westbeek (4-month internship, BSc. EE)

  • Study on benefits of multi-slice analysis for cancer detection in Volumetric Laser Endomicroscopy (VLE) images.

Sjors van Riel (4-month internship, BSc. EE) 

  • Development and implementation of a C++ framework for endoscopic video analysis.

Alexandros Rikos (4-month internship, BSc. EE) 

  • Study on multi-frame voting for cancer detection in Volumetric Laser Endomicroscopy (VLE) images.
  • Publication in preparation for an international conference.

Welcome to my webpage! My name is Fons van der Sommen and I am an assistant professor at Eindhoven University of Technology. My main research interests are Medical Image Analaysis and Computer-Aided Diagnosis, where I aim to bring theory from Machine Learning and Computer Vision to the medical practice.


Contact:  
T:
+31 40 247 3708 

E: This email address is being protected from spambots. You need JavaScript enabled to view it. 
FLUX 5.092, TUE Campus
Eindhoven, the Netherlands

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