38 citations · 58 across the 29 of their papers we have counts for
6 papers · 2 filters
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…
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…
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…
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…
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…