MLMI: Machine Learning in Medical Imaging

More workshops at Medical Image Computing and Computer Assisted Intervention 2026

• AE-CAI / CARE-CAI / OR 2.0: Augmented Environments and Computer-Assisted Interventions — Joint workshop advancing medical imaging and augmented reality for surgical and endoscopic computer-assisted interventions. Covers...
• AFRICAI: Medical Imaging AI in African Health Systems — Annual meeting of MICCAI's Special Interest Group dedicated to advancing medical imaging AI in African...
• AMAI: Applications of Medical AI — Focuses on translational and clinical aspects of AI in healthcare, bringing together researchers, clinicians, and...
• ASMUS: Advances in Simplifying Medical Ultrasound — MICCAI Ultrasound SIG workshop covering advances in AI and computational methods for medical ultrasound imaging...
• BrainWorks: Robust Computational Neuroimaging for Brain Abnormalities — Develops robust computational neuroimaging and neuropathology methods that remain reliable when analyzing brain images containing...
• CAPI-WOMEN: Computer-Assisted Pathology and Imaging for Women's Health — Focuses on imaging and computer-assisted interventions for pathologies specific to women, including gynecological disorders, reproductive...
• CaPTion: Cancer Prevention through Early Detection — Focuses on computational methods for cancer detection and treatment through medical imaging. Addresses challenges in...
• CBM-XXI: Computational Biomechanics for Medicine — Combines mathematical modeling, computer simulation, and medical imaging to solve complex biomedical problems. Explores how...
• CDMRI: Computational Diffusion MRI — Long-running MICCAI workshop focused on computational and machine learning methods for diffusion MRI analysis, including...
• CLiMeM: Continual Learning in Medical Imaging — Focuses on continual and lifelong learning approaches for medical imaging AI that can adapt to...
• CLIP: Clinical Translation of Computational Imaging and Pathology — Focuses on the effective translation of computational imaging techniques into clinical practice by combining imaging...
• CMMCA: Computational Mathematics for Medical Cancer Analysis — Convenes mathematicians, engineers, and physicians to discuss innovative mathematical approaches for analyzing multimodal cancer data....
• COLAS: Collaborative Intelligence and Autonomy in Image-Guided Surgery — Explores how AI and robotic automation can enhance surgical precision and decision-making in image-guided surgery....
• COMPAYL++: Computational Pathology with Multimodal Learning — Integrates digital pathology with machine learning and multimodal clinical data including language, omics, and radiology...
• CREATE: Clinical Robotics and Embodied AI for Therapeutic Environments — Brings together researchers, clinicians, and engineers to explore how embodied AI and large language models...
• DeCaF: Federated and Distributed Learning for Medical Imaging — Addresses federated and distributed learning approaches for medical imaging and healthcare AI that enable collaborative...
• Deep-Brea3th: AI and Imaging for Breast Care — Advances breast cancer detection, segmentation, and treatment through AI and multimodal imaging techniques including mammography,...
• DEMI: Data Engineering in Medical Imaging — Focuses on the data engineering challenges underlying medical imaging AI, including data curation, annotation pipelines,...
• DGM4MICCAI: Deep Generative Models for Medical Image Computing — Applies deep generative models - including diffusion models, GANs, and VAEs - to medical imaging...
• DT4H: Digital Twins for Healthcare — Explores digital twin technology in healthcare, examining how computational models integrating medical imaging, AI, and...
• ELAMI: Efficient and Label-Efficient AI in Medical Imaging — Focuses on developing efficient and label-efficient deep learning methods for medical image analysis that reduce...
• EMA: Efficient Medical AI — Develops computationally efficient and resource-conscious AI solutions for clinical applications, addressing high computational costs, annotation...
• EMERGE: Early-Career Student Research Workshop — Student-led platform for early-career researchers to present original work in medical image computing and computer-assisted...
• EndoLINA: Endoluminal Intervention and Navigation — Addresses the unique challenges of navigating medical endoscopes through tortuous body passages using advanced imaging...
• FAIMI / BRIDGE / EPIMI: Fairness, Regulatory, and Ethical AI in Medical Imaging — Joint workshop covering fairness of AI in medical imaging, bridging regulatory science with imaging evaluation,...
• GRAIL: Graph-Based Methods in Biomedical Image Analysis — Workshop focused on graph-based and topology-informed computational methods for analyzing medical images and biomedical data....
• HAIC: Human-AI Collaboration in Medical Imaging — Focuses on designing AI systems that work effectively alongside clinicians in medical imaging tasks, addressing...
• HemaRAI: AI for Blood Smear and Hematology Image Analysis — Develops robust and clinically reliable AI methods for blood smear analysis, focusing on red blood...
• iMIMIC: Interpretability of Machine Learning for Medical Imaging — Focuses on methods for explaining and interpreting deep learning models in medical imaging, including attribution...
• ISIC: Skin Image Analysis Workshop — Advances computer vision and AI methods for analyzing skin images across dermoscopic, clinical, and 3D...
• MedAgent: Agentic AI for Medicine — Explores how autonomous AI agents can support medical applications including diagnosis, clinical decision-making, intervention, and...
• MedAGI: Medical Artificial General Intelligence — Explores the development of general-purpose AI systems capable of performing diverse medical imaging and clinical...
• METIS: Methods for Translation of Medical Imaging AI to Clinical Settings — Brings together clinical and computational experts to address the gap between developing medical imaging AI...
• MI4MedFM: Mechanistic Interpretability for Medical Foundation Models — Explores mechanistic interpretability techniques - including sparse autoencoders and circuit analysis - to understand how...
• MIART: Medical Imaging and AI for Radiation Therapy — Dedicated forum for researchers applying AI to radiation oncology, focusing on physics-aware and biologically grounded...
• MIRASOL: Medical Imaging in Resource-Constrained Settings — Addresses the development and deployment of medical imaging AI for low-resource clinical environments, including those...
• MISO: Medical Image-Based Spatial Omics — Dedicated MICCAI forum for researchers bridging medical image computing with spatial biology research, exploring how...
• ML-CDS: Multimodal Learning for Clinical Decision Support — Focuses on multimodal learning and fusion across scales for clinical decision support, addressing challenges in...
• MLCN: Machine Learning in Clinical Neuroimaging — Addresses challenges in applying machine learning to neuroimaging data analysis, focusing on methodological approaches for...
• MultiTab: Multimodal AI Integrating Imaging with Tabular Clinical Data — Advances AI methods that integrate medical imaging with tabular clinical data such as electronic health...
• MWM: Medical World Model Workshop — Explores the development of world models for medical applications that can simulate physiological processes, disease...
• ODIN: Multi-Structure and Multi-Modal Learning for 3D Dental Image Analysis — Advances AI techniques for analyzing oral and dental imaging data including CBCT and intraoral scans,...
• OFF-GRID: Continuous Representations and Grid-Free Methods in Medical Imaging — Explores continuous representations and grid-free methods in medical imaging, moving beyond traditional discrete voxel grids...
• OMIA: Ophthalmic Medical Image Analysis — Long-running MICCAI workshop focused on AI and computational methods for ophthalmic image analysis, covering fundus...
• PedAItrics: AI and Imaging for Childhood Diseases and Pediatric Cancers — Advances AI applications in pediatric medical imaging, addressing unique challenges like anatomical variability and limited...
• PIPPI: Perinatal and Paediatric Imaging — Dedicated satellite workshop for sophisticated analysis tools applied to fetal, neonatal, and pediatric imaging data....
• PRiSM: Precision Interventions with Sensing and Mixed Reality — Dedicated to computer-assisted interventions with a strong focus on mixed-reality-enabled surgical guidance. Advances medical imaging...
• RHEUM-AI: AI for Rheumatology Imaging — Focuses on developing AI and medical imaging methods for rheumatic disease detection, monitoring, and management,...
• RIME: Reconstruction and Image Motion Estimation — Addresses the intersection of image reconstruction and motion estimation in medical imaging, advancing computational methods...
• SAFER: Faithful Reasoning and Safe Adaptation in Medical AI — Advances trustworthy medical AI through faithful reasoning and safe adaptation across imaging and multimodal clinical...
• SafeSurg: AI Safety in Surgical Procedures — Brings together researchers and practitioners to advance AI technologies designed to enhance safety in surgical...
• SASHIMI: Simulation and Synthesis in Medical Imaging — Focuses on simulation and synthesis of medical image data using deep learning, machine learning, and...
• ShapeMI: Shape Analysis and Geometric Learning in Medical Imaging — Focuses on shape analysis and geometric learning methods in medical imaging, combining classical computational anatomy...
• STACOM: Statistical Atlases and Computational Modelling of the Heart — Brings together researchers and clinicians focused on statistical analysis of cardiac morphology and dynamics, computational...
• SWITCH+: Surgical Workflow and Instrument Tracking in Computer-assisted Healthcare — Focuses on AI methods for tracking surgical instruments and understanding surgical workflows in computer-assisted interventions....
• TIA: Thoracic Image Analysis — MICCAI satellite event focused on advancing cardiac and pulmonary imaging through interdisciplinary research. Covers all...
• UNSURE: Uncertainty for Safe Utilization of Machine Learning in Medical Imaging — Addresses the critical need to quantify and manage uncertainty in AI systems deployed in clinical...