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.