100: Neural Cellular Automata, Part 2

Awesome NCANCA High Resolution Medical Image Segmentation Networks• MED-NCA: Bio-inspired medical image segmentation (Kalkhof et al.)⁠https://www.sciencedirect.com/science/article/pii/S1361841525001483⁠• Med-NCA: Robust and lightweight segmentation with neural cellular automata (Kalkhof et al.)⁠https://arxiv.org/pdf/2302.03473⁠• M3D-NCA: Robust 3D segmentation with built-in quality control (Kalkhof et al.)⁠https://arxiv.org/pdf/2309.02954⁠ NCA for Medical Image Registration• NCA-Morph: Medical image registration with neural cellular automata (Ranem et al.)⁠https://arxiv.org/pdf/2410.22265⁠ Efficient NCA Inference• OctreeNCA: Single-pass 184 MP segmentation on consumer hardware (Lemke et al.)⁠https://arxiv.org/pdf/2508.06993⁠• eNCApsulate: NCA for precision diagnosis on capsule endoscopes (Krumb et al.)⁠https://arxiv.org/pdf/2504.21562⁠ Edge & Federated Learning with NCA• Unsupervised training of neural cellular automata on edge devices (Kalkhof et al.)⁠https://arxiv.org/pdf/2407.18114⁠• Equitable federated learning with NCA (Lemke and Konstantin et al.)⁠https://arxiv.org/pdf/2506.21735

 Neural Cellular Automata Background• Growing Neural Cellular Automata (Mordvintsev et al.)https://distill.pub/2020/growing-ca/?ref=https://githubhelp.com NCA High Resolution Medical Image Segmentation Networks• MED-NCA: Bio-inspired medical image segmentation (Kalkhof et al.)https://www.sciencedirect.com/science/article/pii/S1361841525001483• Med-NCA: Robust and lightweight segmentation with neural cellular automata (Kalkhof et al.)https://arxiv.org/pdf/2302.03473• M3D-NCA: Robust 3D segmentation with built-in quality control (Kalkhof et al.)https://arxiv.org/pdf/2309.02954 NCA for Medical Image Registration• NCA-Morph: Medical image registration with neural cellular automata (Ranem et al.)https://arxiv.org/pdf/2410.22265 Efficient NCA Inference• OctreeNCA: Single-pass 184 MP segmentation on consumer hardware (Lemke et al.)https://arxiv.org/pdf/2508.06993• eNCApsulate: NCA for precision diagnosis on capsule endoscopes (Krumb et al.)https://arxiv.org/pdf/2504.21562 Edge & Federated Learning with NCA• Unsupervised training of neural cellular automata on edge devices (Kalkhof et al.)https://arxiv.org/pdf/2407.18114• Equitable federated learning with NCA (Lemke and Konstantin et al.)https://arxiv.org/pdf/2506.21735

Jakob Wasserthal, a researcher of Medical AI, located in the University Hospital Basel, Switzerland. Jakob is well-known for his TotalSegmentator.Web: https://totalsegmentator.com/GitHub: https://github.com/wasserth/TotalSegmentator

Paul Barach, an anesthesiologist and critical care physician-scientist as well as public health researcher from Thomas Jefferson University, USA. Paul is translating research into strategies for patient safety and health protection. He has more than 25 years of experience as a practicing physician and physician executive in the military and in academic medical centers. Paul has written the following books 1. Surgical Patient Care2. Human Factors in SurgeryJbara Innovation

Jens Kleesiek is a Professor of Translational Image-guided Oncology in the university hospital Essen. The Focus of Jens’s research is on applying self-supervised and weakly supervised learning paradigms to recognize clinically relevant patterns in large and complex data and the integration of multimodal data sources to enhance the decision-making process at the point of care.

Ghazal Ghazei, a research scientist in Karl Zeiss GmbH. She is focusing on medical AI for eye diagnostics and surgery applications.

Aleksei Tiulpin is an Assistant Professor at the University of Oulu, Finnland and soon to be a professor in Cornell, USA. Aleksei focuses on Intelligent Medical Systems, and develops new machine learning methods for medical applications.

Pingkun Yan is a full professor at the Department of Biomedical Engineering at Rensselaer Polytechnic Institute (RPI), USA. Before joining RPI, he was a Senior Scientist of Philips Research working at the clinical site at the National Institutes of Health (NIH). His research focuses on translational medical imaging informatics and image-guided intervention. 

Marco Lorenzi is a research scientist in the EPIONE team of Inria Sophia Antipolis and Université Côte d’Azur, France. Marco's research is on the study of statistical learning methods to model heterogeneous data in biomedical applications. His group has developed the FedBioMed framework.

Ehsan Adeli is an Assistant Professor from Stanford University, directing the Stanford Translational AI (STAI) in Medicine and Mental Health Lab. Ehsan is also a Co-Director of Stanford AGILE Consortium.Developing ICU Clinical Behavioral Atlas Using Ambient Intelligence and Computer VisionLatent Drifting in Diffusion Models for Counterfactual Medical Image Synthesis