Crowdsourcing in the Medical Context

Abstract

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.

Tasks:

• Build a CNN for Mitotic detection
• Implement a crowdsourcing task for binary classification
• Publish it online for crowdsourcing
• Compare the results with few baselines

Requirements:

• Good understanding of statistics and machine learning methods.
• Very good programming skills in MatConvNet^?!

Location:

• FMI Building, Garching

Resultant Paper

2016
	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. (bib)
	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. (bib)