02.10.2020
MCML at MICCAI 2020
Two Accepted Papers (2 Workshops)
23rd International Conference on Medical Image Computing and Computer Assisted Intervention, Virtual, Oct 04-08, 2020
We are happy to announce that MCML researchers have contributed a total of 2 papers to MICCAI 2020: 2 Workshop papers. Congrats to our researchers!
Workshops (2 papers)
S. Denner • A. Khakzar • M. Sajid • M. Saleh • Z. Spiclin • S. T. Kim • N. Navab
Spatio-temporal learning from longitudinal data for multiple sclerosis lesion segmentation.
BrainLes @MICCAI 2020 - Workshop on Brainlesion: Glioma, Multiple Sclerosis, Stroke and Traumatic Brain Injuries at the 23rd International Conference on Medical Image Computing and Computer Assisted Intervention. Virtual, Oct 04-08, 2020. DOI GitHub
Spatio-temporal learning from longitudinal data for multiple sclerosis lesion segmentation.
BrainLes @MICCAI 2020 - Workshop on Brainlesion: Glioma, Multiple Sclerosis, Stroke and Traumatic Brain Injuries at the 23rd International Conference on Medical Image Computing and Computer Assisted Intervention. Virtual, Oct 04-08, 2020. DOI GitHub
Y. Yeganeh • A. Farshad • N. Navab • S. Albarqouni
Inverse Distance Aggregation for Federated Learning with Non-IID Data.
DART DCL @MICCAI 2020 - Workshop on Domain Adaptation and Representation Transfer, and Distributed and Collaborative Learning at the 23rd International Conference on Medical Image Computing and Computer Assisted Intervention. Virtual, Oct 04-08, 2020. DOI
Inverse Distance Aggregation for Federated Learning with Non-IID Data.
DART DCL @MICCAI 2020 - Workshop on Domain Adaptation and Representation Transfer, and Distributed and Collaborative Learning at the 23rd International Conference on Medical Image Computing and Computer Assisted Intervention. Virtual, Oct 04-08, 2020. DOI
Related
26.08.2026
Transparency for Global Health Aid
MCML PI Stefan Feuerriegel and his team use machine learning to uncover disparities in the global allocation of health aid.
25.08.2026
David Rügamer: We Need Uncertainty Quantification
MCML PI David Rügamer explains why uncertainty quantification is essential for building trustworthy AI and making informed decisions.