TeachingWs18DLMA

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

Hauptseminar/Master Seminar: Deep Learning for Medical Applications

Prof. Nassir Navab, Dr. Shadi Albarqouni; Magda Paschali

Type: Advanced Seminar Course Module IN8901
Type: Master Seminar Module IN2107
SWS: 2+0
ECTS: 5 Credits
Location: CAMP Seminar Room, 03.13.010
Time: Mondays, 10-12
Course Language: English

Announcements

  • 24-08-2018: Paper list is almost ready!
  • 29-06-2018: Registration, through TUM Matching Platform, should be done between 29.06. to 04.07.18
  • 16-06-2018: An introductory meeting: Wednesday, 27.06.2018 (16:00-16:30) in CAMP Seminar Room, 03.13.010.
  • 16-06-2018: Website is up!

Introduction

  • Deep Learning is growing tremendously in Computer Vision and Medical Imaging as well. Highly impacted journals in medical imaging community, i.e. IEEE Transaction on Medical Imaging, published recently their special edition on Deep Learning [1]. The Seminar will propose a list of recent scientific articles related to the main current research topics in deep learning for Medical Applications together with some interesting papers from other communities.

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

In this Master Seminar (formerly Hauptseminar), each student is asked to send three preferences from the list, then he will be assigned one paper. In order to successfully complete the seminar, participants have to fulfill these requirements:

  • Presentation: The selected paper is presented to the other participants (20 minutes presentation 10 minutes questions). Use the CAMP templates for PowerPoint camp-tum-jhu-slides.zip, or Latex: CAMP-latex-template.
  • Written Report: A document of maximum 2 pages should be submitted before the deadline.
  • Attendance: Participants have to participate actively in all seminar sessions.

The students are required to attend each seminar presentation which will be held during this course. Each presentation is followed by a discussion and everyone is encouraged to actively participate. The report must include all references used and must be written completely in your own words. Copy and paste will not be tolerated. Both report and presentation have to be done in English.

You need to upload your presentation together with your report here. A detailed description is written in the Readme file inside the repository. Please create your account on LRZgit and request an access to the repository.

Submission Deadline : You have to submit both the presentation and the written report two weeks right after your presentation session. Your access to the repository will not be any longer granted after the deadline.

Schedule

Date Session: Topic Slides Students
27.06.2018 Preliminary Meeting Slides  
15.10.2018 Paper Assignment    
22.10.2018 No Class    
12.11.2018 Presentation Session 1: Supervised Learning (Detection)    
19.11.2018 Presentation Session 2: Supervised Learning (Segmentation)    
26.11.2018 Presentation Session 3: Unsupervised Learning    
03.12.2018 Presentation Session 4: Semi/Weakly/Webly-Supervised Learning    
10.12.2018 Presentation Session 5: Domain Adaptation    
17.12.2018 Presentation Session 6: Few/Zero-Shot Learning    
07.01.2019 Tentative    
14.01.2018 Presentation Session 7: Uncertainty    
21.01.2019 Conclusion    
28.01.2019 Tentative    

List of Topics and Material

The list of papers:

Topic No Title Conference/Journal Tutor Student (Last name) Link
Supervised Learning (Detection) 1 Learning to Segment Every Thing CVPR 2018 Magda   PDF
  2 Articulated Multi-Instrument 2-D Pose Estimation Using Fully Convolutional Networks TMI 2018 Magda   PDF
  3 Fully automatic detection and segmentation of abdominal aortic thrombus in post-operative CTA images using Deep Convolutional Neural Networks MedIa 2018 Magda   PDF
Supervised Learning (Segmentation) 4 Efficient Annotation of Segmentation Datasets with Polygon-RNN++ CVPR 2018 Ari   PDF
  5 Pelvic Organ Segmentation Using Distinctive Curve Guided Fully Convolutional Networks MedIa 2018 Ari   PDF
  6 Ω-Net (Omega-Net): Fully automatic, multi-view cardiac MR detection, orientation, and segmentation with deep neural networks MedIa 2018 Ari   PDF
Unsupervised Learning 7 Unsupervised Learning by Predicting Noise ICML 2017     PDF
  8 Unsupervised Anomaly Detection with Generative Adversarial Networks to Guide Marker Discovery IPMI 2017     PDF
  9 Unsupervised Representation Learning by Predicting Image Rotations ICLR 2018     PDF
Semi/Weakly/Webly-Supervised Learning 10 WESPE: Weakly Supervised Photo Enhancer for Digital Cameras CVPR 2018     PDF
  11 Weakly Supervised Learning for Whole Slide Lung Cancer Image Classification MIDL 2018     PDF
  12 Semi-supervised multi-organ segmentation via multi-planar co-training BMVC 2018     PDF
Domain Adaptation 13 Cross-Domain Weakly-Supervised Object Detection through Progressive Domain Adaptation CVPR 2018     PDF
  14 Unsupervised Reverse Domain Adaptation for Synthetic Medical Images via Adversarial Training TMI 2018     PDF (Older Version)
  15 Domain Adaptation for MRI Organ Segmentation using Reverse Classification Accuracy MIDL 2018     PDF
Few/Zero-Shot Learning 16 Meta-Learning for Semi-Supervised Few-Shot Classification ICLR 2018     PDF
  17 Prototypical networks for few-shot learning NIPS 2017 Shadi   PDF
  18 Low-Shot Learning from Imaginary Data CVPR 2018     PDF
Uncertainty 19 What Uncertainties Do We Need in Bayesian Deep Learning for Computer Vision? NIPS 2017 Shadi   PDF
  20 Uncertainty quantification using Bayesian neural networks in classification: Application to ischemic stroke lesion segmentation MIDL 2018     PDF
  21 Uncertainty in multitask learning: joint representations for probabilistic MR-only radiotherapy planning
On the Effect of Inter-observer Variability for a Reliable Estimation of Uncertainty of Medical Image Segmentation
MICCAI 2018 Shadi   PDF PDF
  22 A Probabilistic U-Net for Segmentation of Ambiguous Images arXiv 2018 Shadi   PDF

If you are assigned a paper from MICCAI you are requested to present briefly one more work related to the topic that will be determined by your tutor, since MICCAI papers are significantly shorter than the other given options.


MICCAI: Medical Image Computing and Computer Assisted Intervention
CVPR: Conference on Computer Vision and Pattern Recognition
ICLR: International Conference on Learning Representations
TMI: IEEE Transaction on Medical Imaging
JBHI: IEEE Journal of Biomedical and Health Informatics
MedIA: Medical Image Analysis (Elsevier)
TPAMI: IEEE Transactions on Pattern Analysis and Machine Intelligence
BMVC: British Machine Vision Conference
MIDL: Medical Imaging with Deep Learning
NIPS: Neural Information Processing Systems

Literature and Helpful Links

A lot of scientific publications can be found online.

The following list may help you to find some further information on your particular topic:

Some publishers:

Libraries (online and offline):

Some further hints for working with references:

  • JabRef is a Java program for comfortable working with Bibtex literature databases. Handy feature: if you know the PubMed ID for an article, JabRef can import data from there (via "Web Search/Medline").
  • Mendeley is a cross-platform program for organising your references.

If you find useful resources that are not already listed here, please tell us, so we can add them for others. Thanks.


TeachingForm
Title: Deep Learning for Medical Applications
Professor: Prof. Nassir Navab,
Tutors: Dr. Shadi Albarqouni; Magda Paschali
Type: Hauptseminar
Information: Hauptseminar, SWS: 2, ECTS: 5
Term: 2018WiSe
Abstract: Deep Learning is growing tremendously in Computer Vision and Medical Imaging as well. Highly impacted journals in medical imaging community, i.e. IEEE Transaction on Medical Imaging, published recently their special edition on Deep Learning [1]. The Seminar will propose a list of recent scientific articles related to the main current research topics in deep learning for Medical Applications together with some interesting papers from other communities.


Edit | Attach | Refresh | Diffs | More | Revision r1.23 - 27 Aug 2018 - 12:06 - MagdaPaschali

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