TeachingSs19MLMI

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

Practical Course:
Machine Learning in Medical Imaging

Prof. Nassir Navab, Dr. Shadi Albarqouni, Magda Paschali, Anees Kazi, Roger Soberanis, Anjany Sekuboyina, Ashkan Khakzar, Matthias Grimm

Type: Master Practical Course Module IN2106
SWS: 6
ECTS: 10 Credits
Location: CAMP Seminar Room, 03.13.010
Time: Thursdays, 8:15-10
Course Language: English

Announcements:

  • 11-01-2019: An introductory meeting: Monday, 04.02.2019 (12:30-13:15) in CAMP Seminar Room, 03.13.010.
  • 08-01-2019: Website is up!

About the Course

The aim of the course is to provide the students with notions about various machine learning techniques. The course is subdivided into a lecture/excercises block and a project.

  • The lectures will include topics in image processing and analysis, unsupervised methods, and supervised methods. Each lecture will be followed by a practical hands-on exercise (e.g. implementation in Python).
  • The topics of the projects will be distributed at the beginning of the semester. Each topic will be supervised by a different person. The projects are to be realized by couples. The programming language for the projects is to be decided in agreement with your supervisor.

Registration

  • Interested students should attend the introductory meeting to enlist in the course.
  • Students can only register through TUM Matching Platform themselves if the maximum number of participants hasn't been reached (please pay attention to the Deadlines).
  • A maximum number of participants: 20.

Requirements

  • Good knowledge of machine learning methods and terminology: knowledge of the meaning of the words classification, regression, data-set, cross-validation ...
  • Strong knowledge of Python programming language
  • Background in image processing
  • Basic knowledge of C++ is advantageous (in case you decide to use c++ for your final project): knowledge of main concepts of object-oriented programming, basic software engineering, image processing libraries and linear algebra libraries.

Schedule (Tentative)

Date Topic By Slides Homework
04.02.2019 Preliminary Meeting Shadi    
Online Project Assignment Shadi    
25.04.2019 TBA      
02.05.2019 Intro. to Deep Learning Shadi    
09.05.2019 Neural Networks (NN) Roger    
16.05.2019 Convolutional Neural Networks (CNN) Ashkan    
23.05.2019 Convolutional Auto-Encoders (CAE) Matthias Grimm    
06.06.2019 Generative Adversarial Networks (GAN) Azade    
13.06.2019 Intermediate Presentation all tutors    
27.06.2019 Recurrent Neural Networks (RNN/LSTM) Anjany    
04.07.2019 Optional: GCN/Adversarial Attacks Anees/Magda    
11.07.2019 Tentative      
18.07.2019 Final Presentation all tutors    

Evaluation

  • 60% Project Progress (Done by your tutor -- mainly on your weekly progress on lrz git repository)
  • 40% Intermediate and Final Presentation (Done by all tutors -- mainly on your presentation skill, progress so far compared to other groups ...etc.)
  • Presentation: Use the CAMP templates for PowerPoint camp-tum-jhu-slides.zip, or Latex: CAMP-latex-template.

Projects

ID Project Tutor Group I Group II Material




TeachingForm
Title: Machine Learning in Medical Imaging
Professor: Prof. Nassir Navab
Tutors: Dr. Shadi Albarqouni, Magda Paschali, Anees Kazi, Roger Soberanis, Anjany Sekuboyina, Ashkan Khakzar, Matthias Grimm
Type: Praktikum
Information: 6 SWS, 10 ECTS Credits (Module IN2106)
Term: 2019SoSe
Abstract:  


Edit | Attach | Refresh | Diffs | More | Revision r1.11 - 20 Jan 2019 - 14:10 - ShadiAlbarqouni

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