Instructors: Prof. Dr. Nassir NavabDr. Shahrooz Faghih Roohi,  Ashkan Khakzar, Azade Farshad, Roger Soberanis

Time: TBA



  • The presentation and blogpost guidelines are available here: Guide_DLMAws21_22.pdf  
  • The preliminary meeting slides can be found here: DLMASws21-22.pdf
  • The preliminary meeting is scheduled for July 7th, 16:30 (Zoom link is visible on TUMonline).
  • Due to the current situation, the seminar happens in the hybrid form from 12:00 to 14:00 on the dates mentioned in the schedule. The location of the in-person meeting would be at the seminar room: 03.13.010.
  • It is highly recommended to attend the seminar in person. However, for those who cannot attend in person, the online link via Zoom will be provided (the meeting link will be shared with participants via email and on the moodle).


  • 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 (CVPR, NeurIPS, ICCV, ICLR, ICML, ...).

Course Structure

In this Master Seminar (Hauptseminar), students select one scientific article from the list provided by course organizers. The students read the paper, and must accomplish the following:

  • Presentation: The selected paper is presented to the other participants (20 minutes presentation 10 minutes questions). You can use the CAMP templates for PowerPoint, or Latex: CAMP-latex-template.
  • Blog Post: A blog post of 1000-1200 words excluding references should be submitted before the deadline. The blog post must include all references used and must be written completely in your own words. Copy and paste will not be tolerated
  • Attendance: Participants have to participate actively in all seminar sessions. Each presentation is followed by a discussion and everyone is encouraged to actively participate.

Submission Deadline: You have to submit the blog post one week before your presentation session! You should also submit the presentation one day right after your presentation session.

You may look at the blog posts and presentations from the previous semester (using the tree in the upper left corner).



DateSession: TopicSlidesStudents
07.06Preliminary MeetingDLMASws21-22.pdf

Medical Image segmentation / Registration/ Report generation

Meng, Siyue

Vitt, Tobias

Tanida, Tim

Meier, Laura



Engel, Christian

Karaali, Ozan

Radutoiu, Ana

Gafencu, Miruna

Christmas break


Representation Learning

Gomez Sanchez, Joaquin

Bodonhelyi, Anna

Steinmüller, Sebastian


Graphs / Reliabe AI

Petliak Nataliia

Retevoi, Maria-Alexandra

Akhtar, Mohammad Kashif



Elsobky, Omar

Haque, S M Ahasanul


Medical Image segmentation / Registration/ Report generation

Raicea, Radu

List of Topics and Material

Papers in this course are selected from the following venues/publications:

CVPR: Conference on Computer Vision and Pattern Recognition
ICLR: International Conference on Learning Representations
NeurIPS: Neural Information Processing Systems

TPAMI: IEEE Transactions on Pattern Analysis and Machine Intelligence

TMI: IEEE Transaction on Medical Imaging
JBHI: IEEE Journal of Biomedical and Health Informatics
MedIA: Medical Image Analysis (Elsevier)

MICCAI: Medical Image Computing and Computer-Assisted Intervention
BMVC: British Machine Vision Conference
MIDL: Medical Imaging with Deep Learning

List of papers

NoTitleJournal/ ConferenceTutorStudentLink
1Semi-supervised Medical Image Segmentation through Dual-task ConsistencyAAAI 2021ShahroozMeng, Siyue
2Exploring and Distilling Posterior and Prior Knowledge for Radiology Report GenerationCVPR 2021MatthiasTanida, Tim

3Anatomy-Constrained Contrastive Learning for Synthetic Segmentation Without Ground-TruthMICCAI 2021MehdiVitt, Tobias
4Comparison of thyroid segmentation techniques for 3D ultrasoundMedical Imaging 2017: Image ProcessingChrissi, Lennart, MatthiasRaicea, Radu
5Unpaired Training of Deep Learning tMRA for Flexible Spatio-Temporal ResolutionTMI 2021Shahrooz
7Local plasticity rules can learn deep representations using self-supervised contrastive predictionsNeurIPS 2021AshkanRadutoiu, Ana
8Prototypical Contrastive Learning of Unsupervised RepresentationsICLR 2021Tariq
9Improving Contrastive Learning by Visualizing Feature TransformationICCV 2021Yousef

10How Well Do Self-Supervised Models Transfer?CVPR 2021YousefGafencu, Miruna

11Dense Contrastive Learning for Self-Supervised Visual Pre-TrainingCVPR 2021Yousef

12Object-aware Contrastive Learning for Debiased Scene RepresentationarXive 2021 (Google)Yousef
13Public Covid-19 X-ray datasets and their impact on model bias – A systematic review of a significant problemMedIA 2021AshkanSteinmüller, Sebastian
14Shallow Bayesian Meta Learning for Real-World Few-Shot RecognitionICCV 2021Azade
15Exploring Simple Siamese Representation LearningCVPR 2021AzadeGomez Sanchez, Joaquin

16Conditional Deformable Image Registration with Convolutional Neural NetworkMICCAI 2021Anees

17Right for the Right Concept: Revising Neuro-Symbolic Concepts by Interacting with their ExplanationsCVPR 2021Ashkan
18Propagating Uncertainty Across Cascaded Medical Imaging Tasks For Improved Deep Learning InferenceTMI 2021Shahrooz
19How Well do Feature Visualizations Support Causal Understanding of CNN Activations?NeurIPS 2021Ashkan

20Generative Classifiers as a Basis for Trustworthy Computer VisionCVPR 2021HeikoPetliak Nataliia

22TransPath: Transformer-Based Self-supervised Learning for Histopathological Image ClassificationMICCAI 2021AneesElsobky, Omar
23Rethinking Semantic Segmentation from a Sequence-to-Sequence Perspective with TransformersCVPR 2021YousefHaque, S M Ahasanul

24Transformer Interpretability Beyond Attention VisualizationCVPR 2021Yousef

25DualGraph: A graph-based method for reasoning about label noiseCVPR 2021MahsaAkhtar, Mohammad Kashif

27Heterogeneous Graph TransformerACM DLAnees
28pi-GAN: Periodic Implicit Generative Adversarial Networks for 3D-Aware Image SynthesisCVPR 2021FaridKaraali, Ozan

29Neural Deformation Graphs for Globally-consistent Non-rigid ReconstructionCVPR 2021Farid

30Deep learning on ultrasound images of thyroid nodulesBiocybernetics and Biomedical EngineeringChrissi, Lennart, Matthias
31Bayesflow: Learning Complex Stochastic Models with Invertible Neural NetworksIEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS 2020Heiko
32Model-Contrastive Federated LearningCVPR 2021YousefBodonhelyi, Anna

33Autonomic Robotic Ultrasound Imaging System Based on Reinforcement LearningIEEE Transactions on Biomedical EngineeringMariaMeier, Laura

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.

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