38 citations · 58 across the 29 of their papers we have counts for
30 papers
Skillful and realistic precipitation forecasting with reinforced diffusion
Ozan Sener, Anand Avati, David Hafner +7
Accurate precipitation forecasting remains elusive as physics-based models struggle to resolve mesoscale convection and machine learning often yields physically inconsistent, blurr…
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…
Beyond Parameter Space: NTK-Guided Personalized Aggregation for Robust Federated Learning
Mirko Konstantin, Stefan Zachow, Anirban Mukhopadhyay
Federated learning (FL) enables collaborative model training across distributed clients while keeping data local. A central challenge is determining which client updates are benefi…
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…
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…
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…
Distribution-Aware Replay for Continual MRI Segmentation
Nick Lemke, Camila González, Anirban Mukhopadhyay +1
Medical image distributions shift constantly due to changes in patient population and discrepancies in image acquisition. These distribution changes result in performance deteriora…
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…