16.05 – 16.45 Shadi Albarqouni - Can Deep Learning models be trained with annotations collected via Crowdsourcing?
One of the major challenges facing researchers nowadays in applying deep learning (DL) models to Medical Image Analysis is the limited amount of annotated data. Collecting such ground-truth annotations requires domain knowledge (expertise), cost, and time, making it infeasible for large-scale databases. We presented a novel concept for training DL models from noisy annotations collected through crowdsourcing platforms, i.e., Amazon Mechanical Turk, Crowdflower, by introducing a robust aggregation layer to the convolutional neural networks. Our proposed method was validated on a publicly available database on Breast Cancer Histology Images showing interesting results of our robust aggregation method compared to baseline methods, i.e., Majority Voting. In follow-up work, we introduced a novel concept of an image to game-object translation in biomedical Imaging allowing medical images to be represented as star-shaped objects that can be easily embedded to readily available game canvas. The proposed method reduces the necessity of domain knowledge for annotations. Exciting and promising results were reported compared to the conventional crowdsourcing platforms.
CV Shadi Albarqouni is Senior Research Scientist at Chair for Computer Aided Medical Procedures (CAMP) at Technical University of Munich (TUM), Germany. He received his Ph.D. in Computer Science from Technical University of Munich, Germany. He has been working on machine learning with an emphasis on deep learning for medical applications. Albarqouni has published more than 30 papers in both Medical Imaging Computing and Computer Assisted Interventions and presented in IEEE TMI, MICCAI, IPCAI, IJCARS, BMVC, and ICRA. He serves as a reviewer for dozens of top-tier conferences and journals including IEEE TMI, IEEE JBHI, IJCARS and Pattern Recognition. Further, he has been serving as a PC member for a couple of MICCAI workshops during the period 2015-2018. His current research interests include Semi-/Weakly Supervised Deep Learning, Domain Adaptation, and Uncertainty and Explainability of Deep Learning Models.