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

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Hauptseminar/Master Seminar: Deep Learning for Medical Applications

Prof. Nassir Navab, 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: Tuesday 12-14
Course Language: English


  • 10-04-2018: Students are assigned to the preentations sessions.
  • 06-04-2018: Please send us your preferences (at least three preferences) by email to shadi.albarqouni@tum.de by Monday, April 9th. Please make sure the subject of your email is "DLMA_Preferences". Otherwise, it won't be considered.
  • 03-04-2018: Schedule is updated. There is no class on 17th April, 2018.
  • 15-03-2018: List of paers is announced
  • 29-01-2018: Registration, through TUM Matching Platform, should be done between 09.02. to 14.02.18
  • 13-01-2018: An introductory meeting: Monday, 29.01.2018 (11:00-12:00) in CAMP Seminar Room, 03.13.010.
  • 13-01-2018: Website is up!


  • 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.


  • 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.


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.


Date Session (Topic) Slides Students
29.01.2018 Preliminary Meeting slides Students who join this meeting and show their interest will have high priority!
10.04.2018 Paper Assignment    
17.04.2018 Invited Talk about GANs (starts at 1 PM) slides  
24.04.2018 Presentation Session 1 (Understanding CNN)   Gasperini, Frenzel
08.05.2018 Presentation Session 2 (Detection)   Lindae, Shafique, Ganjigunte Nagendra Prasad, Perez Ramirez
29.05.2018 Presentation Session 3 (Segmentation)   Bukas, Arab, Coronel Baracaldo
05.06.2018 Presentation Session 4 (GANs + Adversarial Examples)   Zhang, Vural, Jung, Walger
12.06.2018 Presentation Session 5 (Semi/Weakly-Supervised Learning)   Alguineedy, Fatima, Rajanie Prabha
19.06.2018 Presentation Session 6 (Domain Adaptation)   Rahman, Hackl, Konnerth, Gopal
26.06.2018 No Session    
03.07.2018 No Session    

List of Topics and Material

The list of papers:

No Title Conference/Journal Tutor Student (Last name) Link
1 Cross Modal Distillation for Supervision Transfer CVPR 2016     PDF
2 Detecting Anatomical Landmarks From Limited Medical Imaging Data Using Two-Stage Task-Oriented Deep Neural Networks TMI 2017 Magda Lindae PDF
3 Error Corrective Boosting for Learning Fully Convolutional Networks with Limited Data MICCAI 2017     PDF
4 Simultaneous Deep Transfer Across Domains and Tasks ICCV 2015     PDF
5 Locality Sensitive Deep Learning for Detection and Classification of Nuclei in Routine Colon Cancer Histology Images TMI 2016 Amal Perez Ramirez PDF
6 Automatic 3D Cardiovascular MR Segmentation with Densely-Connected Volumetric ConvNets MICCAI 2017 Roger Coronel Baracaldo PDF
7 DeLiGAN : Generative Adversarial Networks for Diverse and Limited Data CVPR 2017 Stefanie Zhang PDF
8 CYCADA: Cycle-consistent Adversarial Domain Adaptation ICLR 2018 Shadi Hackl PDF
9 Adversarial Discriminative Domain Adaptation CVPR 2017 Stefanie Rahman PDF
10 Unsupervised domain adaptation in brain lesion segmentation with adversarial networks IPMI 2017 Beatrice Konnerth PDF
11 Transfer Learning for Domain Adaptation in MRI: Application in Brain Lesion Segmentation MICCAI 2017 Beatrice Gopal PDF
12 Learning CNNs with Pairwise Domain Adaption for Real-Time 6DoF Ultrasound Transducer Detection and Tracking from X-Ray Images MICCAI 2017     PDF
13 CASED: Curriculum Adaptive Sampling for Extreme Data Imbalance MICCAI 2017     PDF
14 Semi-supervised Learning for Network-Based Cardiac MR Image Segmentation MICCAI 2017 Roger Alguineedy PDF
15 Constrained Deep Weak Supervision for Histopathology Image Segmentation TMI 2017 Magda Fatima PDF
16 Visual and Semantic Knowledge Transfer for Large Scale Semi-supervised Object Detection TPAMI 2017     PDF
17 Improving Landmark Localization with Semi-Supervised Learning CVPR 2018 Shadi Rajanie Prabha PDF
18 End-to-End Adversarial Retinal Image Synthesis TMI 2018 Hasan Vural PDF
19 Anatomically Constrained Neural Networks (ACNNs): Application to Cardiac Image Enhancement and Segmentation TMI 2018 Ari   PDF
20 A deep learning model integrating FCNNs and CRFs for brain tumor segmentation MedIA 2018 Ari Bukas PDF
21 Central focused convolutional neural networks: Developing a data-driven model for lung nodule segmentation MedIA 2017 Ari Arab PDF
22 Anatomical Landmark based Deep Feature Representation for MR Images in Brain Disease Diagnosis JBHI 2018 Gerome Shafique PDF
23 Deep Autoencoding Gaussian Mixture Model for Unsupervised Anomaly Detection ICLR 2018 Hasan Walger PDF
24 Landmark-based deep multi-instance learning for brain disease diagnosis MedIA 2018 Magda Ganjigunte Nagendra Prasad PDF
25 What do Deep Networks Like to See? arXiv 2018 Shadi Gasperini PDF
26 Understanding Measures of Uncertainty for Adversarial Example Detection arXiv 2018 Shadi Jung PDF
27 Bayesian SegNet?: Model Uncertainty in Deep Convolutional Encoder-Decoder Architectures for Scene Understanding<\s> arXiv 2018 Shadi (tentative)   PDF
28 Dynamic routing between capsules NIPS 2017 Anees Frenzel 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
IPMI: International Conference on Information Processing in Medical Imaging

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.

Title: Deep Learning for Medical Applications
Professor: Prof. Nassir Navab
Tutors: Shadi Albarqouni; Magda Paschali
Type: Hauptseminar
Information: IN2107, IN8901
Term: 2018SoSe

Edit | Attach | Refresh | Diffs | More | Revision r1.40 - 25 Jun 2018 - 07:47 - ShadiAlbarqouni

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