TeachingWs2013MLMIPraktikum

Chair for Computer Aided Medical Procedures & Augmented Reality
Lehrstuhl für Informatikanwendungen in der Medizin & Augmented Reality

Practical Course:
Machine Learning in Medical Imaging

Nassir Navab, Diana Mateus, Loic Peter, Sebastian Poelsterl, Pierre Chatelain

Type: Master Practical Course Module IN2106
SWS: 6
ECTS: 10 Credits
Location: MI 03.13.010
Time: Wednesday 10:30-12:00
Course Language: English

About the Course

In this course students will learn through practice the application of different machine learning methods to problems involving medical images. The Master-Praktikum will consist in:

  • (1) a few introductory lectures on machine learning and its application in different problems involving medical imaging,
  • (2) a number of exercises to apply different learning approaches to toy example data,
  • (3) a machine learning project with a real medical application to be solved in groups (pairs).

Regular meetings will be held individually with each group to follow up the progress of the project along the semester.

Information meeting: Wednesday 9th October at 11:00 in the room MI 01.06.020

Evaluation

  • Homework attestations (20%)
  • Practical implementation of the project (50%)
  • Midterm (5%) and final (15%) Presentations:Use the CAMP templates for PowerPoint CAMPmaster.pot, or Latex: CAMP-latex-template.zip.
  • Report (10%): Maximum 8 pages. Please download and use the llncs2e.zip Latex template.

Registration

  • Please register through TUMOnline.
  • Maximum number of participants: 16.

Requirements

  • Basic knowledge in MATLAB and C/C++ programming is an advantage.

Schedule

Date Topic By Slides Homework
09.10.2013 11:00
Room MI 01.06.020
Information Meeting Diana Mateus Info Meeting Slides
Introduction to Probability
 
16.10.2013 Image Representation and Feature Extraction Loic Peter Slides Exercise sheet
Dataset for exercise
23.10.2013 Principal Component Analysis (PCA) and Statistical Shape Models Ahmad Ahmadi Slides Exercise sheet
Dataset and Code for exercise
06.11.2013 Dimensionality Reduction + Presentation of Projects
Ask for attestation date either Tuesday 12 (13:00-15:30) or Thursday 14 (10:00-15:30)
Diana Mateus Slides
Projects
Exercise sheet
Data and code
13.11.2013 Linear Classifiers and Support Vector Machines (SVMs) Sebastian Pölsterl Slides Exercise sheet
Data sets for exercise
Attachment: IRLS
20.11.2013 Evaluation Measures and Bootstrap Sebastian Pölsterl Slides Exercise sheet
27.11.2013 Boosting Pierre Chatelain Slides Exercise sheet
Code for exercise
04.12.2013 Random Forests Loic Peter Slides Exercise sheet
Code for exercise
08.01.2014 Mid-term presentations      
12.02.2014 Final presentations      

Projects

Group Project Supervision
Felix Bork
Christoph Graumann
Segmentation of Brain Tumours in MRI Sebastian
Laura Flores Sanchez
Nicola Leucht
Deep Learning Sebastian
Arianne Tran
Jitinkumar Baghel
Semi-supervised Learning for Interactive Quantification within Histological Images Loic
Luibov Merina
Nawal Alqurashi
Boosting from Several Experts Pierre
Ralf Gutjahr
Juan Sebastian Osorio
Mitosis Shape Model for Generation of Artificial Images Benjamin
Biswajoy Ghosh
Sathish Balakrishnan
Segmentation of Brain Tumours in MRI using Sparse Active Contour Pierre
Poulami Chakrabarti
Shweta Shitole
Construction and Evaluation of a Statistical Shape Model of the Human Skull Benjamin
Johannes Merkle
Daniele Volpi
Supervised Quantification within Histological Images Loic

TeachingForm
Title: Machine Learning in Medical Imaging
Professor: Nassir Navab
Tutors: Diana Mateus, Loic Peter, Sebastian Poelsterl, Pierre Chatelain
Type: Praktikum
Information: 10 ECTS credits
Term: 2013WiSe
Abstract:  


Edit | Attach | Refresh | Diffs | More | Revision r1.25 - 05 Dec 2013 - 10:00 - LoicPeter

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