TeachingWs16MLMI

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, Tingying Peng, Shadi Albarqouni, Sailesh Conjeti, Christian Rupprecht, Nicola Rieke

Type: Master Practical Course Module IN2106
SWS: 6
ECTS: 10 Credits
Location: 03.13.10
Time: 10:00-12:00
Course Language: English

Announcements

  • Final Presentation (7 mins.) will take place on Wednesday, 15th Feb. 2017 at 10 AM in the Seminar room 03.13.10
  • Projects are assigned. Please contact your supervisor!
  • Projects are uploaded (Missing ones will be up soon). Now, you need to send three preferences along with their project's ID to Shadi Albarqouni. Deadline to submit your preferences is Monday, 14th Nov. 2016
  • The preliminary meeting will take place on 4th of July at 11:00 a.m. Seminar room 03.13.10
  • Lectures and assignments are password protected
  • To register for the course, please use the TUM-Matching-System

About the Course

The aim of the course is to provide the students with notions about various machine learning techniques. The course is subdivided in 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 in the beginning of the semester. Each topic will be supervised by a different person. The projects are to be realised in couples. The programming language for the projects is to be decided in agreement with your supervisor.

Schedule

Date Topic By Slides Homework
19.10.2016 Introduction to Machine Learning and Python Wadim Kehl Intro Machine Learning
Intro_Python
 
26.10.2016 Dimensionality reduction and clustering Shadi Albarquoni PCA
PCA_Applications
Clustering
Assignment
data_code
Have fun with faces dataset
02.11.2016 Vollversammlungen im Wintersemester 2016/17 (no lecture)      
09.11.2016 Linear classification Tingying Peng Linear Classifier Assignment
SAheart (should work now)
16.11.2016 Support Vector Machines Diana Mateus Lecture SVMs Assignment
Two moons
Digits
23.11.2016 Random Forests Sailesh Conjeti Lecture Random Forests
Lecture Evaluation Measures
Assignment and Data
7.12.2016 Dies Academics (no lecture)      
14.12.2016 Neural Networks and Autoencoders Abhijit Guharoy Lecture Neural Networks and Autoencoders Assignment and Data
21.12.2016 Deep Learning Christian Rupprecht Lecture CNNs Assignment (matlab + matconvnet)

Assignment (python + tensorflow)
18.01.2017 Attestation All tutors There will be two sessions, please have a look at the schedule below  
         
15.02.2016 Final Presentations      

Evaluation

The grade will be split between weekly exercises associated to each lecture and a final project.
  • The exercises will be evaluated during a mid-term attestation.
  • The evaluation of the projects will include a final presentation

Projects

ID Project Tutor Assigned Student Material
1 Denoising of Magnetic Resonance Spectroscopy Diana Mateus Ruchik Yajnik Slides
2 Cross-modality synthesis through style transfer Diana Mateus Ukrit Wattanavaekin Slides
3 Hetero-Modal Medical Image Segmentation (HeMIS?) Shadi Albarqouni Emerson Tucker, Ahmed Elgazzar Slides
4 Unsupervised Domain Adaptation Shadi Albarqouni Mario Anton Slides
5 Spatial Transformer Networks Sailesh Conjeti Razieh Rezaei Slides
6 Cutting out the middleman - Regressing cytological attributes sans segmentation Sailesh Conjeti Mihai Baltac Slides
7 Super Resolution of Microscopy Images via CNNs (Histology) Abhijit Guha Roy Tamoor Ahmad Slides
8 Deep Retinal Image Understanding Abhijit Guha Roy Adnan Naeem Husain Slides
9 Adversarial Learning Christian Rupprecht Ignacio Sarasúa Slides
10 Intra modality synthesis Christian Rupprecht Faisal Kalim Slides
11 3D Cell Detection using CNN Tingying Peng Nina Stelzl Slides
12 Online Learning with Random Forests for Tool Tracking Nicola Rieke Md Salman Rahman Slides
13 Real-time Super-Resolution in Retinal Microsurgery (Ophthalmology) Nicola Rieke Christopher Kiwus Slides
14 Retinal Vessel Segmentation Nicola Rieke Andrei Militaru Slides

Attestation Schedule

Student Time      
10-11 Ahmad Tamoor
Anton Mario
Baltac Mihai
Elgazzar Ahmed
Husain Adnan
Kalim Faisal
     
11-12 Kiwus Christopher
Militaru Andrei
Sarasua Ignacio
Stelzl Nina
Tucker Emerson
Wattanavaekin Ukrit
     

Registration

  • Interested students should attend the introductory meeting to enlist in the course.
  • Students can only register through TUMOnline themselves if the maximum number of participants hasn't been reached.
  • Maximum number of participants: 16.

Requirements

  • *Strong knowledge of MATLAB, Python * or other programment language
  • Background in image processing
  • Exercises will be held in preferably in Python, althoug an introductory lecture on Python will be given previous knowledge would be advantageous.
  • 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.
  • Basic knowledge of Python is advantageous (in case you decide to use python for your final project): numpy, scipy, scikit-learn.
  • Basic knowledge of naive machine learning methods and terminology: knowledge of the meaning of the words classification, regression, data-set, cross-validation ...

FAQs

  • Should we send the e-mail as a group? Or should each member of the group send it separately?
  • Since we have rather enough projects, every student will be assigned to one project. However, there might be two projects with two members assigned by the course organizers. In short, send your preferences individually.
  • Do we have to sort the options by preference, or are the three of them taken with the same priority?
  • Yes, you need to sort them based on your preferences. We will do our best to match your first preference.
  • For those projects that some knowledge on Deep Learning is required, is it enough with the one we will acquire during the lecture and the assignment or are we supposed to have some extra knowledge?
  • Since the deep learning lecture will be held on 21st Dec., you won't have enough time to work on the project. Therefore, it is recommended to do some extra work.
  • Is the tutor who is assigning each member of the group his/her particular tasks or are we splitting the work?
  • In case if you are assigned with another student as one group, you need to discuss that with your tutor.



TeachingForm
Title: Machine Learning in Medical Imaging
Professor: Nassir Navab
Tutors: Diana Mateus, Tingying Peng, Shadi Albarqouni, Sailesh Conjeti; Christian Rupprecht; Nicola Rieke
Type: Praktikum
Information: 6 SWS, 10 ECTS Credits (IN2106)
Term: 2016WiSe
Abstract:  


Edit | Attach | Refresh | Diffs | More | Revision r1.54 - 07 Feb 2017 - 13:42 - NicolaRieke

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