MLMI2023
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 2023) is the 14th in a series of workshops on this topic in conjunction with MICCAI 2023 as a full-day event on October 8, 2023. 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.
Updates:
2023-10-08: MLMI 2023 successfully concluded today. Thank all the attendees for the support! See you next year!
2023-10-08: Best paper awards announced! The following two papers won the MLMI 2023 Best Paper Awards! Both of them were awarded $500 sponsored by Shanghai United Imaging Intelligence.
Masoud Mokhtari, Neda Ahmadi, Teresa S. M. Tsang, Purang Abolmaesumi, Renjie Liao for the paper entitled “GEMTrans: A General, Echocardiography-based, Multi-Level Transformer Framework for Cardiovascular Diagnosis”
Mengqi Wu, Lintao Zhang, Pew-Thian Yap, Weili Lin, Hongtu Zhu, Mingxia Liu for the paper entitled “Structural MRI Harmonization via Disentangled Latent Energy-Based Style Translation”
2023-09-25: E-posters and 3-min presentation videos of all the Oral and Poster papers are available online!
2023-07-11: Full paper submission due date has been extended to July 21, 2023 (Pacific Time 11:59 PM)
2023-06-06: The submission system of MLMI2023 has been open!
2023-05-01: The website of MLMI2023 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 The technical program will consist of previously unpublished, contributed papers, with substantial time allocated to discussion.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., 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