38 citations · 58 across the 29 of their papers we have counts for
12 papers · 1 filter
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…
ASMR: Angular Support for Malfunctioning Client Resilience in Federated Learning
Mirko Konstantin, Moritz Fuchs, Anirban Mukhopadhyay
Federated Learning (FL) allows the training of deep neural networks in a distributed and privacy-preserving manner. However, this concept suffers from malfunctioning updates sent b…
Don't Reach for the Stars: Rethinking Topology for Resilient Federated Learning
Mirko Konstantin, Anirban Mukhopadhyay
Federated learning (FL) enables collaborative model training across distributed clients while preserving data privacy by keeping data local. Traditional FL approaches rely on a cen…
Extreme Cardiac MRI Analysis under Respiratory Motion: Results of the CMRxMotion Challenge
Kang Wang, Chen Qin, Zhang Shi +46
Deep learning models have achieved state-of-the-art performance in automated Cardiac Magnetic Resonance (CMR) analysis. However, the efficacy of these models is highly dependent on…
Equitable Federated Learning with NCA
Nick Lemke, Mirko Konstantin, Henry John Krumb +3
Federated Learning (FL) is enabling collaborative model training across institutions without sharing sensitive patient data. This approach is particularly valuable in low- and midd…
SG2VID: Scene Graphs Enable Fine-Grained Control for Video Synthesis
Ssharvien Kumar Sivakumar, Yannik Frisch, Ghazal Ghazaei +1
Surgical simulation plays a pivotal role in training novice surgeons, accelerating their learning curve and reducing intra-operative errors. However, conventional simulation tools…
SASVi -- Segment Any Surgical Video
Ssharvien Kumar Sivakumar, Yannik Frisch, Amin Ranem +1
Purpose: Foundation models, trained on multitudes of public datasets, often require additional fine-tuning or re-prompting mechanisms to be applied to visually distinct target doma…
SurGrID: Controllable Surgical Simulation via Scene Graph to Image Diffusion
Yannik Frisch, Ssharvien Kumar Sivakumar, Çağhan Köksal +5
Surgical simulation offers a promising addition to conventional surgical training. However, available simulation tools lack photorealism and rely on hardcoded behaviour. Denoising…
Regulating radiology AI medical devices that evolve in their lifecycle
Camila González, Moritz Fuchs, Daniel Pinto dos Santos +9
Over time, the distribution of medical image data drifts due to factors such as shifts in patient demographics, acquisition devices, and disease manifestations. While human radiolo…
GAUDA: Generative Adaptive Uncertainty-guided Diffusion-based Augmentation for Surgical Segmentation
Yannik Frisch, Christina Bornberg, Moritz Fuchs +1
Augmentation by generative modelling yields a promising alternative to the accumulation of surgical data, where ethical, organisational and regulatory aspects must be considered. Y…
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…
MedSegDiffNCA: Diffusion Models With Neural Cellular Automata for Skin Lesion Segmentation
Avni Mittal, John Kalkhof, Anirban Mukhopadhyay +1
Denoising Diffusion Models (DDMs) are widely used for high-quality image generation and medical image segmentation but often rely on Unet-based architectures, leading to high compu…