Instructors: Prof. Dr. Nassir Navab, Dr. Shadi Albarqouni; Magda Paschali, Ashkan Khakzar


  • 17-07-2019: We would like to encourage you to send us motivation e-mail with title "DLMA_Application" to by the 24th of July 2019.
    We will only evaluate e-mails that follow the template:
    • Name:
    • Master's program:
    • Current Semester:
    • Related courses (if passed, mention the grade):
    • Short Motivation (Max 3 sentences. It should include related projects/publications/competitions/github repositories):
    Please do not attach any documents on your motivation e-mails. Thank you!
  • 05-07-2019: Preliminary meeting: Thursday, 18.07.2019 (13:00-14:00) in CAMP Seminar Room, 03.13.010.
  • 29-06-2019: 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: 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, or Latex: CAMP-latex-template.
  • Blog Post: A blog post of 1000-2000 words excluding references 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 blog post must include all references used and must be written completely in your own words. Copy and paste will not be tolerated. Both the blog post and presentation have to be done in English.

You need to upload your presentation and blog post here. More details will be provided before the beginning of the semester.

Submission Deadline : You have to submit both the presentation and the blog post two weeks right after your presentation session.


DateSession: TopicSlidesStudents
18.07.2019 (13-14)Preliminary MeetingSlides
OnlinePaper Assignment

24.10.2019No Class!

31.10.2019Intro. to our DLMA SeminarGuidelines
07.11.2019Presentation Session 1: Supervised Learning
Ismail, Olefir
14.11.2019Presentation Session 2: Self/Semi/Weakly Supervised Learning
Benito, Burak, Richter
21.11.2019Presentation Session 3: Interpretable ML
Yupeng, Mirac, Acosta
28.11.2019Presentation Session 4: Interpretable ML
Abdelhamid, Berger, Elsharnoby
05.12.2019Presentation Session 5: Misc. Topics: Domain adaptation - Uncertainty
Clement, Panarit
12.12.2019Presentation Session 6: Misc. Topics: Meta Learning - Graph Convolutions
Hongjia, Evren, Nasser
19.12.2019Presentation Session 7: Spatio-Temporal Learning
Benetti, Fok

List of Topics and Material

The list of papers:




TutorStudent (Last name)Link
Supervised Learning1Cardiac Phase Detection in Echocardiograms with Densely Gated Recurrent Neural Networks and Global Extrema LossTMIMariaIsmailPDF

2Fully Convolutional Architectures for Multiclass Segmentation in Chest RadiographsTMIAshkanOlefirPDF


Task Agnostic Meta-Learning for Few-Shot Learning



Self/Semi/Weakly Supervised Learning4Collaborative Learning of Semi-Supervised Segmentation and Classification for Medical ImagesCVPRRogerBenitoPDF

5Self-supervised learning for medical image analysis using image context restorationMedIAMagdaBurakPDF

6FickleNet Weakly and Semi Supervised Semantic Image Segmentation Using Stochastic InferenceCVPRTariqRichterPDF
Interpretable ML (Session 1)7Explainable cardiac pathology classification on cine MRI with motion characterization by semi-supervised learning of apparent flowMedIATariqYupengPDF

8Visual Explanation by Interpretation: Improving Visual Feedback Capabilities of Deep Neural NetworksICLRSeongTaeMiracPDF

9Interpretability Beyond Feature Attribution: Quantitative Testing with Concept Activation Vectors (TCAV)ICMLMahdiAcostaPDF
Interpretable ML (Session 2)10Disentangled representation learning in cardiac image analysisMedIAMagdaAbdelhamidPDF

11Understanding Impacts of High-Order Loss Approximations and Features in Deep Learning InterpretationICMLAshkan

12This Looks Like That: Deep Learning for Interpretable Image RecognitionNeurIPSSeongTaeBergerPDF

13Are Disentangled Representations Helpful for Abstract Visual Reasoning?NeurIPSShadiElsharnobyPDF
Misc. Topics: Domain adaptation - Uncertainty14Unsupervised domain adaptation for medical imaging segmentation with self-ensemblingNeuroImageRogerClementPDF

15Transfusion: Understanding Transfer Learning for Medical ImagingNeurIPSShadiPanaritPDF

16Learning From Noisy Labels By Regularized Estimation Of Annotator ConfusionCVPRShadi

17Can You Trust Your Model’s Uncertainty? Evaluating Predictive Uncertainty Under Dataset ShiftNeurIPSShadi
Misc. Topics: Meta Learning - Graph Convolutions18Learning to Learn How to Learn: Self-Adaptive Visual Navigation Using Meta-LearningCVPRAzadeHongjiaPDF

19Automatic multi-organ segmentation on abdominal ct with dense v-networksTMI




20Exploiting Edge Features in Graph Neural NetworksCVPRHendrikNasserPDF
Spatio-Temporal Learning21Prediction of Disease Progression in Multiple Sclerosis Patients using Deep Learning Analysis of MRI DataMIDLAshkanBenettiPDF

22Predicting Alzheimer’s disease progression using multi-modal deep learning approachNatureGeromeFokPDF

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
NeurIPS: 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.

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