Description & Relevance

Context

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 12-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).
  • Predicting clinical outcome from medical data (genomic, imaging data, etc).

In-brief

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 2022 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’.

Organizers

Keynote Speakers

Prof Alejandro Frangi

University of Leeds

Short bio: Professor Frangi is Diamond Jubilee Chair in Computational Medicine and Royal Academy of Engineering Chair in Emerging Technologies at the University of Leeds, Leeds, UK, with joint appointments at the School of Computing and the School of Medicine. He directs the CISTIB Center for Computational Imaging and Simulation Technologies in Biomedicine. He is Turing Fellow of the Alan Turing Institute. Prof Frangi is the Scientific Director of the Leeds Centre for HealthTech Innovation and Director of Research and Innovation of the Leeds Institute for Data Analytics. Professor Frangi is Chair of the Editorial Board of the MICCAI-Elsevier Book Series (2017-2020) and is Associate Editor of IEEE Trans on Medical Imaging, Medical Image Analysis, SIAM Journal Imaging Sciences, Computer Vision and Image Understanding journals. Professor Frangi was a foreign member of the Review College of the Engineering and Physical Sciences Research Council (EPSRC, 2006-10) in the UK. Professor Frangi's main research interests lie at the crossroads of medical image analysis and modeling with emphasis on machine learning (phenomenological models) and computational physiology (mechanistic models). His highly interdisciplinary work has been translated to cardiovascular, musculoskeletal and neurosciences.

Prof Regina Barzilay

MIT

Short bio: Regina Barzilay is a School of Engineering Distinguished Professor for AI and Health in the Department of Electrical Engineering and Computer Science and a member of the Computer Science and Artificial Intelligence Laboratory at the Massachusetts Institute of Technology. In 2017, she received a MacArthur fellowship, an ACL fellowship and an AAAI fellowship. In 2020, she was awarded the Squirrel AI Award for Artificial Intelligence for the Benefit of Humanity. She is an AI faculty lead for Jameel Clinic, an MIT center for Machine Learning in Health. She also works in natural language processing. She is a recipient of various awards including the NSF Career Award, the MIT Technology Review TR-35 Award, Microsoft Faculty Fellowship and several Best Paper Awards at NAACL and ACL. Her research interests are in machine learning models for molecular modeling with applications to drug discovery and clinical AI.

Prof James C. Gee

University of Pennsylvania

Short bio: Prof Gee is the of Director, Penn Image Computing and Science Laboratory, Department of Radiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA. He is the Co-Director of the Translational Biomedical Imaging Center at the Institute of Translational Medicine and Therapeutics at the University of Pennsylvania. He is also the Director of the Interfaces Program in Biomedical Imaging and Informational Sciences at UPenn.

Dr Serena Yeung

Stanford University

Short bio: I am an Assistant Professor of Biomedical Data Science and, by courtesy, of Computer Science and of Electrical Engineering at Stanford University. My research interests are in the areas of computer vision, machine learning, and deep learning, focusing on applications to healthcare. I lead the Medical AI and Computer Vision Lab (MARVL) at Stanford, and serve as Associate Director of Data Science for the Stanford Center for Artificial Intelligence in Medicine & Imaging (AIMI). I am also affiliated with the Stanford Clinical Excellence Research Center (CERC). Prior to joining the Stanford faculty in 2019, I was a Technology for Equitable and Accessible Medicine (TEAM) Postdoctoral Fellow at Harvard University, where I was hosted by Susan Murphy and John Halamka. I received my Ph.D. from Stanford University in 2018, where I was advised by Fei-Fei Li and Arnold Milstein. During my Ph.D., I also spent time at Facebook AI Research in 2016 and Google Cloud AI in 2017. I additionally co-taught Stanford's CS231N Convolutional Neural Networks course from 2017-2019, with Justin Johnson and Fei-Fei Li.

Dr Pallavi Tiwari

Case Western Reserve University

Short bio: Dr. Pallavi Tiwari is an Assistant Professor of Biomedical Engineering and the director of Brain Image Computing Laboratory at Case Western Reserve University. She is also an associate member of the Case Comprehensive Cancer Center. Dr. Tiwari got her PhD from Rutgers University in 2012. In 2016, she founded the Brain Image Computing lab at Case Western Reserve University. Over the last 15 years, her research has been focused on developing novel image analysis methods for diagnosis, prognosis, and evaluating treatment response of different types of cancers (prostate, breast, lung) and neuro-imaging applications including brain tumors. Her research has so far evolved into over 50 peer-reviewed publications, 50 peer-reviewed abstracts, and 11 patents (6 issued, 5 pending). Dr. Tiwari has been a recipient of several scientific awards, most notably being named as one of 100 women achievers by Government of India for making a positive impact in the field of Science and Innovation. In 2018, she was selected as one of Crain’s Business Cleveland Forty under 40. In 2020, she was awarded the J&J Women in STEM (WiSTEM2D) scholar award in Technology. In 2021, she was awarded the Honorary Early Career Achievement Award through the Society for Imaging informatics in Medicine.

Program Committee Members

Ahmed Nebli, Forschungszentrum Jülich, Germany

Alaa Bessadok, University of Sousse, Tunisia

Chinasa Okolo, Cornell University, USA

Dong Hye Ye, Marquette University, USA

Febrian Rachmadi, RIKEN, Japan

Gang Li, University of North Carolina,USA

Ilwoo Lyu, Vanderbilt University, USA

Jaeil Kim, Kyungpook National University (KNU), Korea

Jiahong Ouyang, Stanford University, USA 

Li Wang, University of North Carolina at Chapel Hill, USA

Lichi Zhang, Shanghai Jiao Tong University, China

Manhua Liu, Shanghai Jiao Tong University, China

Maria A. Zuluaga, EURECOM, France

Melissa Woghiren, University of Alberta, Canada

Pew-Thian Yap, University of North Carolina (UNC), USA

Qian Wang, Shanghai Jiao Tong University, China

Qingyu Zhao, Stanford University, USA

Reza Azad, RWTH University, Germany

Seung Yeon Shin, National Institutes of Health, USA

Ulas Bagci, University of Central Florida (UCF), USA

Won Hwa Kim, POSTECH, Korea

Ziga Spiclin, University of Ljubljana, Slovenia  

Submission

The number of pages can range between 8 and 12 pages including references. Papers should be formatted in Springer LNCS style. PRIME reviewing is double-blind.

The accepted papers will be published in the PRIME LNCS Springer Proceedings.

For paper submission, please use the following link:  https://cmt3.research.microsoft.com/PRIME2022

To download PRIME 2022 flyer, click here.

Key dates:

Full Paper Deadline: June 20, 2022; 11:59 PM EST

Notification of Acceptance: July 16, 2022

Camera-ready Version: July 26, 2022, 11:59 PM PST

Workshop date: September 22, 2022 (8am — 3pm Singapore time)

Submission instructions

Papers should be submitted electronically following the guidelines for authors and LaTeX and MS Word templates available at Lecture Notes in Computer Science, double blind review). Manuscripts should be up to 12 pages and submitted via the PRIME submission websiteNo modifications to the templates are permitted. Failure to abide by the formatting guidelines will result in immediate rejection of the paper.  The papers will be evaluated by three external reviewers or potential inclusion in the scientific program of PRIME-MICCAI.

We have also included a checklist for paper reproducibility.

SPECIAL PRIME 2022: Talented minority scholarship to register accepted PRIME papers by students in low-middle income countries (LMIC).

-We offer scholarships supported by BASIRA Lab to register accepted PRIME papers if the first author is a student at a University in a low-middle income country.  If the rank of your country is larger or equal to 50 based on expenditure on R&D sorted in decreasing order, then you are eligible:

https://en.wikipedia.org/wiki/List_of_countries_by_research_and_development_spending

Program

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