Description & Relevance
Big and complex data is fuelling diverse research directions in both medical image analysis and computer vision research fields. These can be divided into two main categories: (1) analytical methods, and (2) predictive methods. While analytical methods aim to efficiently analyse, represent and interpret data (static or longitudinal), predictive methods leverage the data currently available to predict observations at later time-points (i.e., forecasting the future) or predicting observations at earlier time-points (i.e., predicting the past for missing data completion). For instance, a method which only focuses on classifying patients with mild cognitive impairment (MCI) and patients with Alzheimer’s disease (AD) is an analytical method, while a method which predicts if a subject diagnosed with MCI will remain stable or convert to AD over time is a predictive method. Similar examples can be established for various neurodegenerative or neuropsychiatric disorders, degenerative arthritis or in cancer studies, in which the disease/disorder develops over time.
Why predictive intelligence?
It would constitute a stunning progress in the MICCAI research community if, in a few years, we contribute to engineering a ‘predictive intelligence’ which can map both low-dimensional and high-dimensional medical data onto the future with high precision. This workshop is the first endeavor to drive the field of ‘high-precision predictive medicine’, where late medical observations are predicted with high precision, while providing explanation via machine and deep learning, and statistically, mathematically- or physically-based models of healthy, disordered development and ageing. Despite the terrific progress that analytical methods have made in the last twenty years in medical image segmentation, registration or other related applications, efficient predictive intelligent models/methods are somewhat lagging behind. As such predictive intelligence develops and improves —and this is likely to do so exponentially in the coming years— this will have far-reaching consequences for the development of new treatment procedures and novel technologies. These predictive models will begin to shed light on one of the most complex healthcare and medical challenges we have ever encountered, and, in doing so, change our basic understanding of who we are.
What kind of research problems we aim to solve?
The main aim of PRIME-MICCAI is to propel the advent of predictive models in a broad sense, with application to medical data. Particularly, the workshop will admit 8-page papers describing new cutting-edge predictive models and methods that solve challenging problems in the medical field. We hope that PRIME workshop becomes a nest for high-precision predictive medicine, one that is set to transform multiple fields of healthcare technologies in unprecedented ways.
Topics of interests include but are not limited to predictive methods dedicated to the following topics:
- Modeling and predicting disease development or evolution from a limited number of observations;
- Computer-aided prognostic methods (e.g., for brain diseases, prostate cancer, cervical cancer, dementia, acute disease, neurodevelopmental disorders);
- Forecasting disease/cancer progression over time;
- Predicting low-dimensional data (e.g., behavioral scores, clinical outcome, age, gender);
- Predicting the evolution or development of high-dimensional data (e.g., shapes, graphs, images, patches, abstract features, learned features);
- Predicting high-resolution data from low-resolution data;
- Prediction methods using 2D, 2D+t, 3D, 3D+t, ND and ND+t data;
- Predicting image modality from a different modality (e.g., data synthesis);
- Predicting lesion evolution;
- Predicting missing data (e.g., data imputation or data completion problems).
This workshop will mediate ideas from both machine learning and mathematical/statistical/physical modeling research directions in the hope to provide a deeper understanding of the foundations of predictive intelligence developed for medicine, as well as to where we currently stand and what we aspire to achieve through this field. PRIME-MICCAI 2018 will feature a single-track workshop with keynote speakers with deep expertise in high-precision predictive medicine using machine learning and other modeling approaches —which are believed to stand at opposing directions. Our workshop will also include technical paper presentations, poster sessions, and demonstrations. Eventually, this will help steer a wide spectrum of MICCAI publications from being ‘only analytical’ to being ‘jointly analytical and predictive’.
Short bio: Dinggang Shen is Jeffrey Houpt Distinguished Investigator, and a Professor of Radiology, Biomedical Research Imaging Center (BRIC), Computer Science, and Biomedical Engineering in the University of North Carolina at Chapel Hill (UNC-CH). He is currently directing the Center for Image Analysis and Informatics, the Image Display, Enhancement, and Analysis (IDEA) Lab in the Department of Radiology, and also the medical image analysis core in the BRIC. Dr. Shen’s research interests include medical image analysis, computer vision, and pattern recognition. He has published more than 800 papers in the international journals and conference proceedings. He serves as an editorial board member for eight international journals. He has also served in the Board of Directors, The Medical Image Computing and Computer Assisted Intervention (MICCAI) Society, in 2012-2015. He is Fellow of IEEE, and also Fellow of The American Institute for Medical and Biological Engineering (AIMBE).
Short bio: Ender Konukoglu studied Electrical Engineering at Bogazici University, Turkey, and did a PhD on medical image analysis at INRIA Sophia Antipolis, France. He worked at Microsoft Research Cambridge as a post-doctoral researcher and at Athinoula A. Martinos Center / Harvard Medical School as junior faculty. In 2016, he joined ETH Zurich as an assistant professor of biomedical image computing. His research focuses on machine learning in medical imaging and biophysical models.
Short bio: Dr Ipek Oguz is a Research Associate in the Department of Radiology at the University of Pennsylvania, where she is a member of the Penn Image Computing and Science Laboratory (PICSL) and Center for Biomedical Image Computing and Analytics (CBICA). She received her Ph.D. in Computer Science at the University of North Carolina at Chapel Hill. Her research is in the field of medical image analysis and specifically in the development of novel methodology for quantitative medical image analysis, with applications to neuroimaging, including Huntington’s disease and multiple sclerosis. Her technical interests include graph-based segmentation methods and longitudinal studies. She has co-authored more than 50 peer-reviewed journal and conference publications. She is an executive in the Women in MICCAI Committee and a co-chair of IPMI 2017.
Short bio: Kilian M. Pohl received the PhD degree from the Computer Science and Artificial Intelligence Laboratory, Massachusetts Institute of Technology. He is currently the Program Director of Biomedical Computing at the Center for Health Sciences, SRI International. His research focuses on creating algorithms aimed at identifying biomedical phenotypes accelerating the mechanistic understanding, diagnosis, and treatment of neuropsychiatric disorders.
Program Committee Members
Ahmed Fetit, University of Dundee, UK
Daniel Rueckert, Imperial College London, UK
Dong Nie, University of North Carolina (UNC), USA
Gang Li, University of North Carolina (UNC), USA
Gerard Sanroma, Pompeu Fabra University, Spain
Guorong Wu, University of North Carolina (UNC), USA
Hamid Soltanian-Zadeh, University of Tehran, Iran and Henry Ford Hospital, USA
Ilwoo Lyu, Vanderbilt University, USA
Jaeil Kim, Kyungpook National University (KNU), Korea
Le Lu, NVidia Corp, USA
Li Wang, University of North Carolina (UNC), USA
Marc Niethammer, University of North Carolina (UNC), USA
Mehdi Moradi, IBM Research, USA
Mert Sabuncu, Cornell University, USA
Morteza Mardani, Stanford University, USA
Polina Golland, Massachusetts Institute of Technology (MIT), USA
Qian Wang, Shanghai Jiao Tong University (SJTU), China
Qingyu Zhao, Stanford University, USA
Serena Yeung, Stanford University, USA
Stefanie Demirci, Technische Universität München (TUM), Germany
Tal Arbel, McGill University, Canada
Ulas Bagci, University of Central Florida (UCF), USA
Yinghuan Shi, Nanjing University, China
YingYing Zhu, Cornell University, USA
Yu Zhang, Stanford University, USA
Yue Gao, Tsinghua University, China
Ziga Spiclin, University of Ljubljana, Slovenia
Papers are limited to eight pages, and formatted in Springer LNCS style. PRIME reviewing is double-blind.
Full Paper Deadline: June 11, 2018
Notification of Acceptance: July 15, 2018
Camera-ready Version: July 20, 2018
Workshop date: Sept 16, 2018
To download PRIME flyer click on PRIME-MICCAI_2018_flyer_final.
Best PRIME-MICCAI Paper Award: