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 2021) is the 12th in a series of workshops on this topic in conjunction with MICCAI 2021 as a full-day event on September 27, 2021. 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.
Accepted papers have been published in LNCS proceedings!
2021-09-27: MLMI 2021 successfully concluded today. Please visit the Pathable page for the recordings. Thank all the attendees for the support! See you next year!
2021-09-27: Best paper awards announced! The following two groups won the MLMI 2021 Best Paper Awards! Each of them will be awarded $500 sponsored by Shanghai United Imaging Intelligence.
Mingxin Jiang, Shimin Yang, Zhongbo Zhao, Jiadong Yan, Yuzhong Chen, Tuo Zhang, Shu Zhang, Benjamin Becker, Keith M Kendrick, and Xi Jiang for the paper entitled “Exploring Gyro-Sulcal Functional Connectivity Differences across Task Domains via Anatomy-Guided Spatio-Temporal Graph Convolutional Networks”
Jie Wei, Yongsheng Pan, Yong Xia, and Dinggang Shen for the paper entitled “Learning to Synthesize 7T MRI from 3T MRI with Few Data by Deformable Augmentation”
MLMI 2021 offers scholarships covering the paper registration fee for first authors in low-middle-income countries.
Congratulations to Islem Mhiri from Université de Sousse.
2021-09-09: The program has been released. Detailed instructions about presentations are coming soon! Thank you!
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 The technical program will consist of previously unpublished, contributed papers, with substantial time allocated to discussion.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
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