Medical Image Computing and Computer Assisted Intervention – MICCAI 2026 Satellite Events. Responsible AI in Medical Image Computing

Beschreibung

This multi-volume set LNCS 17251-17279 constitutes the proceedings from the satellite events held in conjunction with the 29th International Conference on Medical Image Computing and Computer Assisted Intervention, MICCAI 2026, in Strasbourg, France, during September 27-October 1, 2026.

The 1,571 papers included in these 29 volumes were thoroughly reviewed and accepted for presentation at 112 workshops, tutorials, and challenges. The proceedings include the following thematic parts:

Parts I-II: Computational Pathology, Imaging Genomics, and Federated Learning;

Parts III-V: Neuroimaging, Neurooncology, and Neuroscience;

Part VI: Women's Health (Breast and Pelvic Image Analysis);

Part VII: Fetal, Neonatal, and Pediatric Image Analysis;

Part VIII: Generative AI in Medical Imaging; 

Part IX: Shape Analysis in Medical Imaging;

Part X: Uncertainty for Safe utilisation of Machine learning in Medical Imaging;

Parts XI-XII: Foundation Models, World Models, and Multimodal Learning;

Part XIII: Agentic AI and Multimodal Learning;

Parts XIV-XVI: Low Resource Environments, Efficient AI, and Next Generation Training;

Parts XVII-XIX: Cardiac and Thoracic Image Analysis;

Part XX: Dental and Opthalmic Image Analysis;

Part XXI: Head and Neck, Liver, Rheumatologic, and Skin Image Analysis;

Parts XXII-XXIV: Machine Learning and Medical Image Analysis in Clinical Applications;

Part XXV: Computer Assisted Interventions and Digital Twins;

Part XXVI: Ultrasound Imaging and Data Engineering Solutions in Medical Imaging;

Part XXVII: Medical Image Reconstruction, Registration, and Motion Estimation;

Part XXVIII: Computational Methods and Graphs for Biomedical Image Analysis;

Part XXIX: Responsible AI in Medical Image Computing.

Autor*in

Spyridon Bakas

Themen in »Medical Image Computing and Computer Assisted Intervention – MICCAI 2026 Satellite Events. Responsible AI in Medical Image Computing«

Interpretability Deep Learning Explainability medical image analysis Fairness Regulatory science Bias AI governance AI Ethics Responsible AI Philosophy of medical imaging Human-AI collaboration AI for healthcare Healthcare Human factors Human skill enhancement

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