TeachingSs2015MLMIPraktikum

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, Lichao Wang, Tingying Peng, Diana Mateus, Loic Peter, Benjamin Gutierrez Becker, Shadi Albarqouni, Fausto Milletarì

Type: Master Practical Course Module IN2106
SWS: 6
ECTS: 10 Credits
Location: MI 03.13.010
Time: Tuesday 16:00-17:30
Course Language: English

Announcements

  • To register for the course, please use the TUM-Matching-System.
  • January 21st, 2015: Preliminary meeting was held on January 21st, 15:00-15:30pm in MI 03.13.010.

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

Evaluation

  • Homework attestations (25%)
  • Project (75%), including:
  • Practical implementation
  • Midterm and final Presentations:Use the CAMP templates for PowerPoint CAMPmaster.pot, or Latex: CAMP-latex-template.zip.
  • Report : Maximum 8 pages. Please download and use the llncs2e.zip Latex template.

Registration

  • Interested students should attend the introductory meeting to enlist in the course.
  • Students in the waiting list of summer term would have priority for registration.
  • Students can only register through TUMOnline themselves if the maximum number of participants hasn't been reached by end of August.
  • Maximum number of participants: 20.

Requirements

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

Preliminary Schedule

Date Topic By Slides Homework
21.01.2015 Introduction Meeting Lichao Wang Projects  
14.04.2015 Image Representation and Feature Extraction Loic Peter Slides Exercise1
21.04.2015 Linear Classifiers and Support Vector Machines (SVMs) Benjamin G Becker Slides Exercise2
28.04.2015 Evaluation Measures and Bootstrap Sebastian Pölsterl Slides Exercise3
05.05.2015 Introduction to Deep Learning Felix Achilles    
19.05.2015 Boosting and Random Forests Tingying Peng and Loic Peter BoostingSlides

Forest Slides
BoostingExercise
02.06.2015 Principal Component Analysis (PCA) and Statistical Shape Models Fausto Milletari Slides Exercise6
Code
09.06.2015 Clustering Lichao Wang Slides Exercises
Data
16.06.2015 Introduction to Sparse Methods Shadi Albarqouni SlidesI SlidesII
23.06.2015 Attestation All Tutors    
14.07.2015 Final Presentation All Tutors    

Group Project Supervision
Linda Bui, Kristina Erhard, Subash Thapa MS lesion segmentation Shadi
Manish Kumar Mishra, Edengenet Dejene Deep learning for mitochondria classification Lichao
Vishal Bhalla, Ching-Yu Kao, Ritaja Sengupta Multi-modal similarity deep networks Diana
Akshit Malhotra, Anshul Vij Multiple instance learning by boosting Tingying
Nils Schwenzfeier Super resolution using dictionary learning Tingying & Fausto
Gerda Bortsova, Florian Dubost Unsupervised domain adaptation Loic
Hannah Wester Robust statistical shape model Benjamin
Stefan Marinov, Francisco Esteban Vicente Boosting from multiple experts Loic

TeachingForm
Title: Machine Learning in Medical Imaging
Professor: Prof. Dr. Nassir Navab
Tutors: Lichao Wang, Tingying Peng, Diana Mateus, Loic Peter,Benjamin Gutierrez Becker, Shadi Albarqouni, Fausto Milletarì
Type: Praktikum
Information: 10 ECTS credits
Term: 2015SoSe
Abstract:  


Edit | Attach | Refresh | Diffs | More | Revision r1.29 - 10 Oct 2015 - 14:13 - FaustoMilletari

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