Hari Trivedi: Performance drop of Commmercial Radiology AI in Clinic

Hari Trivedi is an associate professor in Radiology and Imaging Sciences at Emory University, USA. Hari is the co-director of the Health Innovation and Translational Informatics (HITI) lab where he works on machine learning in radiology.Real-world performance evaluation of a commercial deep learning model for intracranial hemorrhage detectionAI in Action: A Road Map From the Radiology AI Council for Effective Model Evaluation and Deployment

Pierre Jannin is a legend from the MICCAI society for his research in Image-guided Surgery. Pierre is an INSERM Research Director, located in Rennes France. From 2012, he has been heading the Inserm Research group MediCIS. Pierre is designing and developing computer assisted surgery systems for 30+ years. Towards responsible research in digital technology for health care

Mohammad Yaqub is an Associate Professor in Mohammad Bin Zayed University of AI in Abu Dhabi. Mohammad works on problems in medical image analysis, radiomics and radiogenomics. He investigates continual learning, adversarial attacks and defense, and studies  healthcare challenges using natural language processing. Mohammad is the general chair of MICCAI 2026.

Nassir Navab is a professor of computer-aided medical procedures and augmented reality in TU Munich. His work involves developing technologies to improve the quality of medical intervention and bridges the gap between medicine and computer science.After studying mathematics and physics, computer engineering and systems control, Prof. Navab did his doctorate at INRIA / Paris XI. He then did two years of postdoctoral research at MIT Media Laboratory in Cambridge, USA. Prior to becoming a full professor at TUM in 2003, Prof. Navab was a distinguished member of the technical staff at Siemens Corporate Research (SCR) in Princeton, USA. In 2006, he became a board member of MICCAI, the organizer of the world’s leading conference on medical image computing and computer assisted intervention. He is on the editorial board of many international journals, including IEEE TMI, MedIA and Medical Physics. Prof. Navab has authored hundreds of scientific publications and has filed over 60 international patents.

Francesco Ciompi is an Associate Professor and Research Group Leader in AI for Precision Medicine at the Department of Pathology of Radboud University Medical Center in Nijmegen, the Netherlands. His main research focus is AI for precision medicine in the area of pathology, with applications to discovery and implementation of predictive and prognostic biomarkers in immuno oncology, and computer aided diagnosis for large-scale digital pathology and multi-modal data.

Miguel Angel Gonzalez Ballister is a professor at the Universitat Pompeu Fabra in Barcelona, where he founded the famous Barcelona Centre for New Medical Technologies, BCN Medtech. Miguel has more than 400 publications in peer-reviewed scientific journals and conferences, and has supervised 20+ PhD theses. 

Sophia Bano is an associate professor in Robotics and Artificial Intelligence in the Department of Computer Science, University College London. Sophia is doing award winning research on medical robotics, and organizing IPCAI 25 and 26.

Bisesh Khanal is the director of NAAMII Nepal and founder of Abhinavnepal. Bisesh did his PhD in INRIA Sophia Antipolis and PostDoc in London. His dream is to bring world class AI research in Nepal.

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