TeachingWs19MLMI

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, Roger Soberanis, Ashkan Khakzar, Walter Simson, Magda Paschali, Azade Farshad

Type: Master Practical Course Module IN2106
SWS: 6
ECTS: 10 Credits
Location: Seminar Room, 03.13.010
Time: Thursdays, 16-18
Course Language: English

Announcements:

  • 23-09-2019: Projects are assigned! Please contact your tutor this week to define a regular weekly meeting.
  • 13-09-2019: Projects are announced! Please, send us your preferences (at least three preferences) by email to mlmi@mailnavab.in.tum.de before September, 20th. Please make sure the subject of your email is "MLMI_Projects_Preferences".
  • 05-07-2019: Preliminary meeting: Thursday, 18.07.2019 (12:00-13:00) in CAMP Seminar Room, 03.13.010.
  • 29-06-2019: 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. the 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

Schedule

Date Topic By Slides Homework
18.07.2019 Preliminary Meeting Shadi Slides  
Online Project Assignment Shadi    
24.10.2019 Intro. to DL (Tentative) Shadi    
31.10.2019 Neural Networks (NN) Roger    
07.11.2019 Convolutional Neural Networks (CNN) Ashkan    
14.11.2019        
21.11.2019 Generative Models (CAE, VAE, GANs) Azade    
28.11.2019 (tentative) Recurrent Models (RNN, LSTM) Anjany?    
05.12.2019 Robustness Magda    
12.12.2019 Intermediate Presentation all tutors Guidelines  
19.12.2019        
06.02.2020 Final Presentation all tutors Guidelines  

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
1 Comparison of Uncertainty Estimation techniques in Deep Learning for Medical Applications Agnieszka and Shadi Andrei Diaconu, Springer Abdelhamid, Esposito Project Description
2 Nerve Segmentation and Classification on Ultrasound and Optoacoustic Imaging Magda and Walter Flynn, Giri Yagubbayli, Alamleh Project Description
3 Weakly supervised medical segmentation: How to save the annotation cost for training deep segmentation network? SeongTae     Project Description
4 SIIM-ACR Pneumothorax Segmentation Challenge Ashkan and Roger Jahiri, Beheim Ergin, Faizan Project Description
5 Robustness in Wasserstein Generative Adversarial Network Azade Naeem, Karaoglu Khanduja, Deo Project Description
6 Deep learning with adaptive feature acquisition Gerome Raj, Bernecker Elsharnoby, Mullakaeva Project Description




TeachingForm
Title: Machine Learning in Medical Imaging
Professor: Prof. Nassir Navab
Tutors: Dr. Shadi Albarqouni, Roger Soberanis, Ashkan Khakzar, Walter Simson, Magda Paschali, Azade Farshad
Type: Praktikum
Information: 6 SWS, 10 ECTS Credits (Module IN2106)
Term: 2019WiSe
Abstract:  


Edit | Attach | Refresh | Diffs | More | Revision r1.13 - 25 Sep 2019 - 11:32 - AzadeFarshad

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