38 citations · 58 across the 29 of their papers we have counts for
16 papers · 1 filter
Applications of Neural Cellular Automata: State of the Art, Challenges and Opportunities
Nick Lemke, Niklas Ihm, John Kalkhof +11
Neural Cellular Automata (NCAs) are a new type of neural network architecture which enable accurate and robust inference at extremely small model sizes. Recently, NCAs have advance…
Coarse to Fine: Iterative Adversarial Neural Cellular Automata for Medical Image Synthesis
Anh Thi Luu, Nick Lemke, Anirban Mukhopadhyay
Large-scale, publicly available datasets have driven advances in deep learning, but privacy and legal restrictions often limit data sharing in medical imaging. Synthetic data gener…
Sterilizable Scene Graph Generation for Operating Rooms
Nick Lemke, Ssharvien Kumar Sivakumar, Antoine P. Sanner +4
Scene graph generation from surgical video enables a holistic and structured understanding of surgical scenes by modeling objects and their semantic relationships. Despite recent a…
Group Equivariant Diffusion for Anomaly Detection in Computational Cytology
Swarnadip Chatterjee, Ssharvien Kumar Sivakumar, Anirban Mukhopadhyay
Computational cytology on whole-slide images is challenging because malignant cells are rare, heterogeneous, and annotated slides are scarce. Anomaly detection frameworks can be tr…
SWoMo: Neuro-Symbolic World Model for Cataract Surgery Simulation
Ssharvien Kumar Sivakumar, Akwele Johnson, Anirudh Dhingra +3
Realistic surgical simulation plays a crucial role in training novice surgeons and in the development of autonomous agents. World models can scale such simulation environments to r…
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…
NCA-Morph: Medical Image Registration with Neural Cellular Automata
Amin Ranem, John Kalkhof, Anirban Mukhopadhyay
Medical image registration is a critical process that aligns various patient scans, facilitating tasks like diagnosis, surgical planning, and tracking. Traditional optimization bas…
Frequency-Time Diffusion with Neural Cellular Automata
John Kalkhof, Arlene Kühn, Yannik Frisch +1
Despite considerable success, large Denoising Diffusion Models (DDMs) with UNet backbone pose practical challenges, particularly on limited hardware and in processing gigapixel ima…
Continual atlas-based segmentation of prostate MRI
Amin Ranem, Camila González, Daniel Pinto dos Santos +3
Continual learning (CL) methods designed for natural image classification often fail to reach basic quality standards for medical image segmentation. Atlas-based segmentation, a we…
From Pointwise to Powerhouse: Initialising Neural Networks with Generative Models
Christian Harder, Moritz Fuchs, Yuri Tolkach +1
Traditional initialisation methods, e.g. He and Xavier, have been effective in avoiding the problem of vanishing or exploding gradients in neural networks. However, they only use s…
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…