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
31.07.2026
Benedikt Wiestler: We Want to Build a Time Machine
MCML PI Benedikt Wiestler explains how AI models help to develop specific strategies in clinical therapy for brain tumor patients.
28.07.2026
Gaps in the Benchmark: Why Medical AI Fails in the Real World
MCML researchers show in Nature Health how dynamic red teaming exposes hidden weaknesses in Medical AI beyond static benchmarks.
