TeachingWs18MLMI

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, Christoph Baur, Magda Paschali, Huseyin Coskun, Abhijit Guha-Roy, Anees Kazi, Roger Soberanis, Anjany Sekuboyina

Type: Master Practical Course Module IN2106
SWS: 6
ECTS: 10 Credits
Location: CAMP Seminar Room, 03.13.010
Time: Mondays, 12-14
Course Language: English

Announcements:

  • 29-06-2018: Registration, through TUM Matching Platform, should be done between 29.06. to 04.07.18
  • 19-06-2018: A tentative schedule is up!
  • 16-06-2018: An introductory meeting: Wednesday, 27.06.2018 (12:00-12:30) in CAMP Seminar Room, 03.13.010.
  • 16-06-2018: 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
27.06.2018 Preliminary Meeting Shadi Slides  
15.10.2018 Machine Learning I Shadi    
22.10.2018 Machine Learning II Shadi    
29.10.2018 Neural Networks (NN) Roger    
05.11.2018 Convolutional Neural Networks (CNN) Shadi    
12.11.2018 Convolutional Auto-Encoders (CAE) Abhijit    
19.11.2018 Generative Adversarial Networks (GAN) Christoph    
26.11.2018 Security of Deep Learning under Adversarial Settings Magda    
03.12.2018 Graph Convolutional Networks (GCN) Anees (Tentative)    
10.12.2018 Recurrent Neural Networks (RNN/LSTM) Anjany    
17.12.2018 Intermediate Presentation all tutors    
07.01.2019 Tentative      
14.01.2019 Tentative      
21.01.2019 Tentative      
28.01.2019 Final Presentation all tutors    

Evaluation (Tentative)

The grade will be split between weekly exercises associated with each lecture and a final project.
  • The exercises will be evaluated during a mid-term attestation. The impact of the exercises on the final grade is 30%.
  • The evaluation of the projects will include a final presentation. The impact of the final project on the final grade is 70%.
  • 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, Christoph Baur, Magda Paschali, Huseyin Coskun, Abhijit Guha-Roy, Anees Kazi, Roger Soberanis, Anjany Sekuboyina
Type: Praktikum
Information: 6 SWS, 10 ECTS Credits (Module IN2106)
Term: 2018WiSe
Abstract:  


Edit | Attach | Refresh | Diffs | More | Revision r1.9 - 03 Aug 2018 - 15:53 - ShadiAlbarqouni

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