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, Christoph Baur

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, 12-14
Course Language: English


  • 18-10-2017: See Requirements (below)!
  • 09-10-2017: Papers are assigned!
  • 03-10-2017: The pool of papers is ready! Please send us your preferences along with the corresponding ID (at least three preferences) to "shadi.albarqouni@tum.de" by latest 6th October 2017. Please use this title "DLMA_Preferences" when you send your preferences.
  • 10-07-2017: Please register via TUM Matching System within 14.07.2017 - 19.07.2017 .
  • 26-06-2017: An introductory meeting: Monday, 10.07.2017 (15:30-16:00) in CAMP Seminar Room, 03.13.010.
  • 26-06-2017: 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: 18.


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. 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 Topic Slides Students
10.07.2017 Preliminary Meeting slides Students who join this meeting and show their interest will have high priority!
16.10.2017 Paper Assignment    
23.10.2017 Kick-Off Session slides Review
06.11.2017 Presentation Session 1   Patwari, Hartmann, Rana, Haninger
13.11.2017 Presentation Session 2   Schukraft, Einarsson , Haradhun, Gong
04.12.2017 Presentation Session 3   Muneer Ahmad, Pinheiro Pereira, Arsalan, Tamoor
11.12.2017 Presentation Session 4   Kowatsch, Smits Serena, Tselousov
08.01.2018 Presentation Session 5   Saha, Barthel, De Almeida Lirio Dourado
15.01.2018 Presentation Session 6
Evaluation Session (15 mins)
  Bohare, Sedra, Kasperek

List of Topics and Material

The list of papers:

No Title Conference/Journal Tutor Student Link
1 Automatic Vertebra Labeling in Large-Scale 3D CT using Deep Image-to-Image Network with Message Passing and Sparsity Regularization IPMI 2017 Anjany   PDF
2 SpineNet?: Automatically Pinpointing Classification Evidence in Spinal MRIs MICCAI 2016 Anjany Einarsson PDF
3 Beyond a gaussian denoiser: Residual learning of deep cnn for image denoising. IEEE TIP 2017 Sai Rana PDF
4 Fast convolutional neural network training using selective data sampling: Application to hemorrhage detection in color fundus images. IEEE TMI 2016 Hasan Schukraft HTML
5 Adversarial training and dilated convolutions for brain MRI segmentation. arXiv 2017 Mehrdad Hartmann PDF
6 Spectral Graph Convolutions on Population Graphs for Disease Prediction arXiv 2017 Ahmad De Almeida Lirio Dourado PDF
7 SurvivalNet?: Predicting patient survival from diffusion weighted magnetic resonance images using cascaded fully convolutional and 3D convolutional neural networks MICCAI 2017 Beatrice Haradhun PDF
8 MDNet: A Semantically and Visually Interpretable Medical Image Diagnosis Network CVPR 2017 Shadi Pinheiro Pereira PDF
9 ChestX?-ray8: Hospital-scale Chest X-ray Database and Benchmarks on Weakly-Supervised Classification and Localization of Common Thorax Diseases CVPR 2017 Shadi Sedra PDF
10 3D deeply supervised network for automated segmentation of volumetric medical images MICCAI 2017 Mehrdad Patwari HTML
11 Deep Adversarial Networks for Biomedical Image Segmentation Utilizing Unannotated Images MICCAI 2017 Beatrice Gong HTML
12 Deep Learning for Sensorless 3D Freehand Ultrasound Imaging MICCAI 2017 Mehrdad   HTML
13 DeepIGeoS?: A Deep Interactive Geodesic Framework for Medical Image Segmentation arXiv 2017 Ahmad Barthel PDF
14 CASED: Curriculum Adaptive Sampling for Extreme Data Imbalance MICCAI 2017 Magda Muneer Ahmad HTML
15 Explaining and Harnessing Adversarial Examples arXiv 2015 Magda Bohare PDF
16 Dipole: Diagnosis Prediction in Healthcare via Attention-based Bidirectional Recurrent Neural Networks ACM 2017 Gerome Smits Serena PDF
17 Learning what to look in chest X-rays with a recurrent visual attention model arXiv 2017 Gerome Tselousov PDF
18 Deep MR to CT Synthesis using Unpaired Data MICCAI 2017 Christoph Arsalan PDF
19 Deep Generative Adversarial Neural Networks for Realistic Prostate Lesion MRI Synthesis arXiv 2017 Christoph Tamoor PDF
20 Learning Loss Functions for Semi-supervised Learning via Discriminative Adversarial Networks arXiv 2017 Shadi (3/3)   PDF
21 Learning from Simulated and Unsupervised Images through Adversarial Training CVPR 2017 Christoph Kowatsch PDF
22 Low Dose CT Image Denoising Using a Generative Adversarial Network with Wasserstein Distance and Perceptual Loss arXiv 2017 Sai Haninger PDF
23 Sharpness-aware Low dose CT denoising using conditional generative adversarial network arXiv 2017 Sai   PDF
24 Multi-Input Cardiac Image Super-Resolution using Convolutional Neural Networks MICCAI 2016 Amal   HTML
25 An artificial agent for anatomical landmark detection in medical images MICCAI 2016 Ahmad Saha PDF
26 Fine-tuning Convolutional Neural Networks for Biomedical Image Analysis: Actively and Incrementally CVPR 2017 Shadi Kasperek PDF

ICML: International Conference on Machine Learning
TMI-SIDL: IEEE Transaction on Medical Imaging, Special Issue on Deep Learning
ISBI: International Symposium on Biomedical Imaging
NIPS: Neural Information Processing Systems
ICRA: International Conference on Robotics and Automation
IJCARS: International Journal on Computer Assisted Radiology and Surgery
JBHI: IEEE Journal of Biomedical and Health Informatics
MICCAI: Medical Image Computing and Computer Assisted Intervention

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, Christoph Baur
Type: Hauptseminar
Information: Hauptseminar, SWS: 2, ECTS: 5
Term: 2017WiSe

Edit | Attach | Refresh | Diffs | More | Revision r1.34 - 08 Nov 2017 - 14:46 - ShadiAlbarqouni

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