2 citations · 2 across the 4 of their papers we have counts for
4 papers
OctreeNCA: Single-Pass 184 MP Segmentation on Consumer Hardware
Nick Lemke, John Kalkhof, Niklas Babendererde +1
Medical applications demand segmentation of large inputs, like prostate MRIs, pathology slices, or videos of surgery. These inputs should ideally be inferred at once to provide the…
Federated-Continual Dynamic Segmentation of Histopathology guided by Barlow Continuity
Niklas Babendererde, Haozhe Zhu, Moritz Fuchs +2
Federated- and Continual Learning have been established as approaches to enable privacy-aware learning on continuously changing data, as required for deploying AI systems in histop…
Exploring SAM Ablations for Enhancing Medical Segmentation in Radiology and Pathology
Amin Ranem, Niklas Babendererde, Moritz Fuchs +1
Medical imaging plays a critical role in the diagnosis and treatment planning of various medical conditions, with radiology and pathology heavily reliant on precise image segmentat…
Jointly Exploring Client Drift and Catastrophic Forgetting in Dynamic Learning
Niklas Babendererde, Moritz Fuchs, Camila Gonzalez +2
Federated and Continual Learning have emerged as potential paradigms for the robust and privacy-aware use of Deep Learning in dynamic environments. However, Client Drift and Catast…