MLMI2026

Overview

Machine learning plays a crucial role in the medical imaging field, including but not limited to computer-aided diagnosis, image segmentation, image registration, image reconstruction, image fusion, image-guided therapy, image annotation, and image retrieval. The workshop main scope is to help advance scientific research within the broad field of machine learning in medical imaging. MLMI also focuses on new trends and unsolved challenges in this area.   The workshop aims to facilitate translating machine learning boosted medical imaging research from bench to bedside. Topics of interests include, but are not limited to, machine learning methods (e.g., deep learning, statistical learning, ensemble learning, sparse learning, multi-task learning, multi-view learning, relational learning, and manifold learning methods, generative learning) with their applications to medical image analysis, computer-aided diagnosis, multi-modality fusion, image reconstruction, image retrieval, cellular image analysis, large-scale medical imaging data analytics, molecular imaging, and digital pathology.  The first academic objective of the workshop is to bring together researchers in medical imaging, machine learning, pattern recognition, computer vision, and artificial intelligence communities to discuss the new techniques of machine learning and their applications in medical imaging. The second objective is to explore new paradigms of biomedical image analysis systems that exploit the latest advancement in machine learning and pattern recognition. MLMI 2026 will feature a single-track workshop with keynote speaker(s), technical paper presentations, poster sessions, and demonstrations of the state-of-the-art machine learning systems and concepts that are applied to the analysis of medical images.

News: 

• 2026-04-09: Our website is now online!

Obejective

Our goal is to advance scientific research within the broad field of machine learning in medical imaging. The technical program will consist of previously unpublished, contributed papers, with substantial time allocated to discussion. We are looking for original, high-quality submissions on innovative researches and developments in medical image analysis using machine learning techniques.

Topics

Topics of interests include but are not limited to machine learning methods (e.g., probabilistic models, deep learning, weakly supervised learning, reinforcement learning, predictive models, large language/vision models, etc.) with their applications to (but not limited) the following areas:

• • Image analysis of anatomical structures and lesions

  • Computer-aided detection/diagnosis

  • Multi-modality fusion for diagnosis, image analysis, and image-guided interventions

  • Medical image reconstruction

  • Medical image retrieval

  • Cellular image analysis

  • Molecular/pathologic image analysis

  • Dynamic, functional, and physiologic imaging