MLMI2022

Machine learning plays an essential role in the medical imaging field, including computer-aided diagnosis, image segmentation, image registration, image fusion, image-guided therapy, image annotation, and image database retrieval. Machine Learning in Medical Imaging (MLMI 2022) is the 13th in a series of workshops on this topic in conjunction with MICCAI 2022 as a full-day event on September 18, 2022. This workshop focuses on major trends and challenges in this area, and it presents original work aimed to identify new cutting-edge techniques and their applications in medical imaging.

2022-09-18: MLMI 2022 successfully concluded today. Please visit the Pathable page for the recordings. Thank all the attendees for the support! See you next year!

2022-09-18: Best paper awards announced! The following two groups won the MLMI 2022 Best Paper Awards! Each of them will be awarded $500 sponsored by Shanghai United Imaging Intelligence.

• • Samuel Joutard, Reuben Dorent, Sebastien Ourselin, Tom Vercauteren, and Marc Modat for the paper entitled “Driving Points Prediction For Abdominal Probabilistic Registration”

  • Hao Guan, Siyuan Liu, Weili Lin, Pew-Thian Yap, and Mingxia Liu for the paper entitled “Fast Image-Level MRI Harmonization via Spectrum Analysis”

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 The technical program will consist of previously unpublished, contributed papers, with substantial time allocated to discussion.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 of interests include but are not limited to machine learning methods (e.g., statistical methods, deep learning, weakly supervised learning, reinforcement learning, extreme learning machines, 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