MLMI2022
Overview
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”
2022-09-16: The workshop program has been updated. The event will kick off at 8:00 am (SGT or GMT+8) on September 18, 2022. You can access it online on Pathable platform. See you all soon!
2022-08-29: The program has been released. Detailed instructions about presentations are coming soon! Thank you!
2022-06-14: Full paper submission due date has been extended to June 22, 2022 (Pacific Time 11:59 PM)
2022-06-01: The submission system of MLMI2022 has been open!
2022-04-10: The website of MLMI2022 has been online!
Objective
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 The technical program will consist of previously unpublished, contributed papers, with substantial time allocated to discussion.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., 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