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

THIS WEBPAGE IS DEPRECATED - please visit our new website

Hauptseminar/Master Seminar: Deep Learning for Medical Applications

Prof. Nassir Navab, Dr. Shadi Albarqouni; Ari Tran, Ashkan Khakzar

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: Thursdays, 12-14
Course Language: English


  • 24-04-2019: Papers are assigned!
  • 17-04-2019: List of Papers is announced! Please, send us your preferences (at least three preferences) by email to shadi.albarqouni@tum.de by Tuesday, April 23rd. Please make sure the subject of your email is "DLMA_Papers_Preferences". Otherwise, it won't be considered.
  • 05-02-2019: Registration, through TUM Matching Platform, should be done between 07.02.19 to 13.02.19
  • 11-01-2019: An introductory meeting: Monday, 04.02.2019 (13:15-14:00) in CAMP Seminar Room, 03.13.010.
  • 08-01-2019: Website is up!


  • Deep Learning is growing tremendously in Computer Vision and Medical Imaging as well. Highly impacted journals in the 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: 15.


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 the 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 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
04.02.2019 Preliminary Meeting Slides  
Online Paper Assignment    
25.04.2019 No Class    
02.05.2019 No Class    
09.05.2019 Presentation Session 1: Supervised and Unsupervised Learning   Natalia, Subhadarshini, Asli
16.05.2019 Presentation Session 2: Semi/Weakly/Webly-Supervised Learning   Louise, Anindya, Anna
23.05.2019 Presentation Session 3: Interpretability of Deep Learning Models   Polina, Ghalia, Cristina
06.06.2019 Presentation Session 4: Selected topics (Domain Adaptation / Meta Learning / Uncertainty)   Yan-Chi, Ahmed, Dhaval
27.06.2019 Presentation Session 5: Selected topics (Domain Adaptation / Meta Learning / Uncertainty)   Amir, Sebastian, John
04.07.2019 No Class    

List of Topics and Material

The list of papers:

Topic No Title Conference/Journal Tutor Student (Last name) Link
Supervised Learning 1 SynSeg?-Net: Synthetic Segmentation Without Target Modality Ground Truth TMI     PDF
  2 BiSeNet?: Bilateral Segmentation Network for Real-time Semantic Segmentation ECCV     PDF
  3 Focus, Segment and Erase: An Efficient Network for Multi-Label Brain Tumor Segmentation ECCV     PDF
  4 Learning a Probabilistic Model for Diffeomorphic Registration TMI     PDF
  5 Efficient Multiple Organ Localization in CT Image using 3D Region Proposal Network TMI Ashkan Natalia PDF
  6 Explicit topological priors for deep-learning based image segmentation using persistent homology IPMI     PDF
  7 Real-Time Patient-Specific Lung Radiotherapy Targeting using Deep Learning IPMI     PDF
  8 Adaptive Graph Convolution Pooling for Brain Surface Analysis IPMI Anees Subhadarshini PDF
Unsupervised Learning 9 Zero-Annotation Object Detection with Web Knowledge Transfer ECCV Matthias   PDF
  10 Unsupervised two-path neural network for cell event detection and classification using spatio-temporal patterns TMI     PDF
  11 Unsupervised Deformable Registration for Multi-Modal Images via Disentangled Representations IPMI Hasan Asli PDF
  12 A cross-center smoothness prior for variational Bayesian brain tissue segmentation IPMI     PDF
Semi/Weakly/Webly-Supervised Learning 13 Joint Weakly and Semi-Supervised Deep Learning for Localization and Classification of Masses in Breast Ultrasound Images TMI Tariq Louise PDF
  14 GANsfer Learning: Combining labelled and unlabelled data for GAN based data augmentation arxiv Magda   PDF
  15 Training recurrent neural networks robust to incomplete data: Application to Alzheimer’s disease progression modeling MedIA? SeongTae   PDF
  16 Thoracic Disease Identification and Localization with Limited Supervision CVPR Ashkan Anindya PDF
  17 Semi-Supervised and Task-Driven Data Augmentation IPMI Shadi   PDF
  18 Semi-Supervised Brain Lesion Segmentation with an Adapted Mean Teacher Model IPMI Roger Anna PDF
  19 Ultrasound Image Representation Learning by Modeling Sonographer Visual Attention IPMI     PDF
Interpretability of Deep Learning Models 20 GAN Dissection: Visualizing and Understanding Generative Adversarial Networks arxiv Neda Polina PDF
  21 Visual Feature Attribution using Wasserstein GANs CVPR     PDF
  22 Interpret Neural Networks by Identifying Critical Data Routing Paths CVPR Magda Ghalia PDF
  23 Learning Interpretable Anatomical Features Through Deep Generative Models: Application to Cardiac Remodeling MICCAI     PDF
    Automatic brain tumor grading from MRI data using convolutional neural networks and quality assessment MICCAI Neda   PDF
  24 Sanity Checks for Saliency Maps NIPS Ashkan   PDF
  25 Human-in-the-Loop Interpretability Prior NIPS Shadi   PDF
  26 Melanoma Recognition via Visual Attention IPMI SeongTae Cristina PDF
  27 Efficient Interpretation of Deep Learning Models Using Graph Structure and Cooperative Game Theory: Application to ASD Biomarker Discovery IPMI     PDF
Selected topics (Domain Adaptation / Meta Learning / Uncertainty) 28 MILD-Net: Minimal Information Loss Dilated Network for Gland Instance Segmentation in Colon Histology Images MedIA? Marie Piraud Yan-Chi PDF
  29 Taskonomy: Disentangling Task Transfer Learning CVPR Magda John PDF
  30 Unsupervised Reverse Domain Adaptation for Synthetic Medical Images via Adversarial Training TMI     PDF
  31 Zero-Shot Object Detection ECCV Roger Dhaval PDF
  32 Training Medical Image Analysis Systems like Radiologists MICCAI     PDF
  33 Learning-Based Quality Control for Cardiac MR Images TMI     PDF
  34 Riemannian geometry learning for disease progression modelling IPMI SeongTae Amir PDF
  35 ImageNet?-trained CNNs are biased towards texture; increasing shape bias improves accuracy and robustness ICLR Anees Sebastian PDF
  36 Meta-Learning Update Rules for Unsupervised Representation Learning ICLR     PDF
  37 Learning deep representations by mutual information estimation and maximization ICLR Hasan Ahmed PDF
  38 Learning Robust Representations by Projecting Superficial Statistics Out ICLR     PDF
  39 Cox-PASNet: Pathway-based Sparse Deep Neural Network for Survival Analysis BIBM Marie Piraud   PDF
    Predicting clinical outcomes from large scale cancer genomic profiles with deep survival models Scientific Report     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.

Title: Deep Learning for Medical Applications
Professor: Prof. Nassir Navab
Tutors: Dr. Shadi Albarqouni; Ashkan Khakzar, Ari Tran
Type: Hauptseminar
Information: Hauptseminar, SWS: 2, ECTS: 5
Term: 2019SoSe

Edit | Attach | Refresh | Diffs | More | Revision r1.44 - 05 Jul 2019 - 11:54 - ShadiAlbarqouni

Lehrstuhl für Computer Aided Medical Procedures & Augmented Reality    rss.gif