5 citations · 7 across the 15 of their papers we have counts for
15 papers
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…
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…
SMART: A Flexible, Interpretable, and Scalable Spatio-temporal Brain Atlas from High-Resolution Imaging Data
John Kalkhof, Boris Gutman, Emile d'Angremont +2
We introduce SMART, a framework for learning a flexible, interpretable, and scalable spatio-temporal brain atlas from longitudinal high-resolution 3D medical images. Existing appro…
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…
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…
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…
NCAdapt: Dynamic adaptation with domain-specific Neural Cellular Automata for continual hippocampus segmentation
Amin Ranem, John Kalkhof, Anirban Mukhopadhyay
Continual learning (CL) in medical imaging presents a unique challenge, requiring models to adapt to new domains while retaining previously acquired knowledge. We introduce NCAdapt…
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…
Unsupervised Training of Neural Cellular Automata on Edge Devices
John Kalkhof, Amin Ranem, Anirban Mukhopadhyay
The disparity in access to machine learning tools for medical imaging across different regions significantly limits the potential for universal healthcare innovation, particularly…
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…
M3D-NCA: Robust 3D Segmentation with Built-in Quality Control
John Kalkhof, Anirban Mukhopadhyay
Medical image segmentation relies heavily on large-scale deep learning models, such as UNet-based architectures. However, the real-world utility of such models is limited by their…
Med-NCA: Robust and Lightweight Segmentation with Neural Cellular Automata
John Kalkhof, Camila González, Anirban Mukhopadhyay
Access to the proper infrastructure is critical when performing medical image segmentation with Deep Learning. This requirement makes it difficult to run state-of-the-art segmentat…
Detecting respiratory motion artefacts for cardiovascular MRIs to ensure high-quality segmentation
Amin Ranem, John Kalkhof, Caner Özer +2
While machine learning approaches perform well on their training domain, they generally tend to fail in a real-world application. In cardiovascular magnetic resonance imaging (CMR)…
Disentanglement enables cross-domain Hippocampus Segmentation
John Kalkhof, Camila González, Anirban Mukhopadhyay
Limited amount of labelled training data are a common problem in medical imaging. This makes it difficult to train a well-generalised model and therefore often leads to failure in…