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, Sailesh Conjeti

Type: Advanced Seminar Course Module IN8901
Type: Master Seminar Module IN2107
SWS: 2+0
ECTS: 4 Credits
Location: Seminar room 03.13.010
Time: 14-16 (tentative)
Course Language: English


  • 19-01-2017: Submission Deadline for both Report and Presentation is 31st January 2017 at 23:59.
  • 02-11-2016: Date Assignment Complete: Please check your time slot.
  • 28-10-2016: Paper Assignment (Complete). Please approach your tutor by email if you have any question!
  • 25-10-2016: We have a meeting tomorrow to assign the papers.
  • 05-08-2016: Preference List: Students are kindly asked to submit their preferences (3 topics) to Shadi Albarqouni starting from 17 October 2016 till 25 October 2016.
  • 05-08-2016: Uptaing the topic list: We may update the list after the MICCAI Conference in October 2016.
  • 04-07-2016: Registration is open from 01.07.2016 to 06.07.2016 through the TUM Matching Platform.
  • 29-06-2016: Tentative Schedule is up!
  • 29-06-2016: The list of topics is up!
  • 29-06-2016: An introductory meeting: Wednesday, 29 June 2016 at 11 AM (30 minutes) in CAMP Seminar Room, 03.13.010.


  • 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.



In this Master Seminar (formerly Hauptseminar), each student is asked to select one paper from a list. 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.zip.
  • Written Report: A document of maximum 2 pages is written and submitted one week after the presentation. Please download and use the llncs2e.zip Latex template.
  • 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.

Schedule (Tentative)

Date Time Topic Slides Students
2016-06-29 11:00 Preliminary meeting Slides  
2016-10-26 14-16 Intro. CNN Lecture (Tentative)/Paper Assignment    
2016-11-23 14-16 Presentations   Haris Jabbar, Matthias Kühne, Dominik Durner
2016-11-30 14-16 Presentations   Moustafa Aboulatta, Emerson Tucker
2016-12-14 14-16 Presentations   Ahmed El-Gazzar, Tamoor Ahmad, Sara Haj
2017-12-21 14-16 Presentations   Atanas Mirchev, Anna Listsyna, Faisal Kalim
2017-01-11 14-16 Presentations   Sanjeev Kumar, Quniji Zhou, Paulius Sukys
2017-01-18 14-16 Presentations   Georgi Dikov, Mihai Baltac, Josue Page

List of Topics and Material

The list of papers:

No Application Title Conference/Journal Tutor Student Link
1 Lesion Segmentation Parallel Multi-Dimensional LSTM, With Application to Fast Biomedical Volumetric Image Segmentation NIPS-15 Felix Ahmed El-Gazzar Paper
2 - Deep 3D Convolutional Encoder Networks With Shortcuts for Multiscale Feature Integration Applied to Multiple Sclerosis Lesion Segmentation TMI-SIDL-16     Paper
3 - V-Net: Fully Convolutional Neural Networks for Volumetric Medical Image Segmentation arXiv Shadi   arXiv
4 Lesion Detection Deep Convolutional Neural Networks for Computer-Aided Detection: CNN Architectures, Dataset Characteristics and Transfer Learning TMI-SIDL-16 Shadi Sanjeev Kumar Paper, arXiv
5 - Improving Computer-aided Detection using Convolutional Neural Networks and Random View Aggregation TMI-SIDL-16 Shadi Emerson Tucker Paper, arXiv
6 - 1) Iterative Multi-domain Regularized Deep Learning for Anatomical Structure Detection and Segmentation from Ultrasound Images
2) Automatic polyp detection in colonoscopy videos using an ensemble of convolutional neural networks
MICCAI-16, ISBI-15     Paper1
7 - Computer-Aided Diagnosis with Deep Learning Architecture: Applications to Breast Lesions in US Images and Pulmonary Nodules in CT Scans nature-16 Diana Quniji Zhou Paper
8 Learning Methodology Combining Generative and Discriminative Representation Learning for Lung CT Analysis With Convolutional Restricted Boltzmann Machines TMI-SIDL-16   Georgi Dikov Paper
9 - Unsupervised Deep Learning Applied to Breast Density Segmentation and Mammographic Risk Scoring TMI-SIDL-16 Sailesh Tamoor Ahmad Paper
10 Transfer Learning & Fine Tuning 1) Off-the-shelf convolutional neural network features for pulmonary nodule detection in computed tomography scans
2) Chest pathology detection using deep learning with non-medical training
ISBI-15 Abhijit Paulius Sukys Paper1
11 Training Data Consideration Fast Convolutional Neural Network Training Using Selective Data Sampling: Application to Hemorrhage Detection in Color Fundus Images TMI-SIDL-16 Sailesh Matthias Kühne Paper
12 Annotation Learning to Read Chest X-Rays: Recurrent Neural Cascade Model for Automated Image Annotation CVPR-16 Ahmad Moustafa Aboulatta Paper, arXiv
13 - Interleaved Text/Image Deep Mining on a Large-Scale Radiology Database CVPR-15, JMLR Ahmad Mihai Baltac Paper, arXiv
14 Crowdsourcing AggNet: Deep Learning From Crowds for Mitosis Detection in Breast Cancer Histology Images TMI-SIDL-16 Shadi   Paper
15 Registration A CNN Regression Approach for Real-Time 2D/3D Registration TMI-SIDL-16 Sailesh Faisal Kalim Paper
16 - Pixel Recurrent Neural Networks ICML-16 Ahmad Atanas Mirchev arXiv
17 - Standard Plane Localization in Fetal Ultrasound via Domain Transferred Deep Neural Networks JBHI-15 Abhijit Dominik Durner Paper
18 Surgical Workflow EndoNet?: A Deep Architecture for Recognition Tasks on Laparoscopic Videos arXiv (TBA) Sailesh Josue Page arXiv
19 - TSC-DL: Unsupervised Trajectory Segmentation of Multi-Modal Surgical Demonstrations with Deep Learning ICRA-16     Paper
20 - Query-by-example surgical activity detection IJCARS-16 Felix Sara Haj Paper
21 Similarity Learning Deep similarity learning for multimodal medical images DLMA Diana Anna Listsyna Paper
22   HeMIS?: Hetero-Modal Image Segmentation MICCAI-16 Shadi Haris Jabbar arXiv

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; Sailesh Conjeti; Maximilian Baust
Type: Hauptseminar
Information: Hauptseminar, SWS: 2, ECTS: 4
Term: 2016WiSe

Edit | Attach | Refresh | Diffs | More | Revision r1.45 - 19 Jan 2017 - 16:11 - ShadiAlbarqouni

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