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

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Crowdsourcing for Healthcare

Crowdsourcing for Healthcare

Scientific Director: Nassir Navab

Contact Person(s): Shadi Albarqouni

Keywords: Medical Imaging, Machine Learning for Medical Applications, Crowdsourcing


Today's clinical procedures often generate a large amount of digital images requiring close inspection. Manual examination by physicians is time-consuming and machine learning in computer vision and pattern recognition is playing an increasing role in medical applications. In contrast to pure machine learning methods, crowdsourcing can be used for processing big data sets, utilising the collective brainpower of huge crowds. Since individuals in the crowd are usually no medical experts, preparation of medical data as well as an appropriate visualization to the user becomes indispensable. The concept of gamification typically allows for embedding non-game elements in a serious game environment, providing an incentive for persistent engagement to the crowd. Medical image analysis empowered by the masses is still rare and only a few applications successfully use the crowd for solving medical problems. The goal of this project is to bring the gamification and crowdsourcing to the Medical Imaging community.


Figure 1: AggNet? Framework
Figure 2: Playsourcing Framework


S. Matl, R. Brosig, M. Baust, N. Navab, S. Demirci
Vascular Image Registration Techniques: A Living Review
Medical Image Analysis, Volume 35, pp. 1-7, 2017 (bib)
S. Albarqouni, S. Matl, M. Baust, N. Navab, S. Demirci
Playsourcing: A Novel Concept for Knowledge Creation in Biomedical Research
Proceedings of MICCAI Workshop on Large-scale Annotation of Biomedical data and Expert Label Synthesis, Athens, Greece, October 2016
The first two authors contribute equally to this paper.
S. Albarqouni, C. Baur, F. Achilles, V. Belagiannis, S. Demirci, N. Navab
AggNet: Deep Learning from Crowds for Mitosis Detection in Breast Cancer Histology Images
IEEE Transactions on Medical Imaging (TMI), Special Issue on Deep Learning, vol. 35, no. 5, pp. 1313 - 1321, 2016.
The first two authors contribute equally to this paper.
D. Volpi, M. H. Sarhan, R. Ghotbi, N. Navab, D. Mateus, S. Demirci
Online Tracking of Interventional Devices for Endovascular Aortic Repair
International Journal of Computer Assisted Radiology and Surgery, June 2015, Volume 10, Issue 6, pp 773-781 (bib)


Contact Person(s)

Shadi Albarqouni
Dr. Shadi Albarqouni

Working Group

Christoph Baur
Christoph Baur
Maximilian Baust
Dr. Maximilian Baust
Shadi Albarqouni
Dr. Shadi Albarqouni
Stefanie Demirci
Dr. Stefanie Demirci
Stefan Matl
Stefan Matl


Vasileios Belagiannis
Dr. Vasileios Belagiannis


internal project page

Please contact Shadi Albarqouni for available student projects within this research project.

Project Phases

Phase Period Conducted by Materials Results so far Code
AggNet? 04/2015-10/2015 Christoph Baur Done TMI Paper soon
PlaySourcing? 09/2015-01/2016 Stefan Matl Done MICCAI-Workshop demo


Useful Links

Edit | Attach | Refresh | Diffs | More | Revision r1.14 - 13 Oct 2016 - 12:33 - ShadiAlbarqouni

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