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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 2024 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

CDRH & FDA

Short bio: Ghada currently works as a staff scientist at the Office of Science and Engineering Laboratories (OSEL) within the FDA's Center for Devices and Radiological Health (CDRH). Her research at FDA focuses on creating methods and tools for evaluating, regulating, and monitoring AI-enabled medical devices. Prior to this role, she was a researcher at the National Institutes of Health, where she devised computational health models to address challenges faced by vulnerable populations such as infants, and marginalized communities. Ghada earned her Ph.D. from the University of South Florida (USF) in 2018, after completing her master there in 2013. With a PhD in Machine Learning and Masters in Computer Science and Affective Science, her diverse background, spanning industry, academia, and government, blends advanced technical skills with practical applications and interdisciplinary insights. With a publication record of 30+ journal articles, 25+ conference papers, and 3 patents, she's been acknowledged with awards such as MIT Inventors under 35 and IEEE Computational Life Sciences. As an engaged global scholar, Ghada actively collaborates with international peers, organizes symposiums and workshops at leading venues, participates in several mentoring programs, and ardently advocates for the empowerment of women and minorities in STEM.

University of Cape Town

Short bio: Dr. Tinashe Mutsvangwa is currently the head of the Data Science Department and a Full Professor at IMT Atlantique, France, where he oversees the development of new research and teaching initiatives. Concurrently, he holds an Adjunct Appointment at the University of Cape Town (UCT), South Africa. Previously, he served as the Deputy Director of the Biomedical Engineering Division at UCT and led the Medical Image-based Inferencing and Distributed Diagnosis (Mi2d2) Lab. Dr. Mutsvangwa holds a PhD in Biomedical Engineering from UCT and has contributed extensively to the fields of medical image analysis, particularly in predictive modeling and 3D reconstruction techniques. His leadership in the Computational Omics and Biomedical Informatics Program underscores his commitment to developing data science capabilities that address health challenges in Africa through innovative educational and research frameworks. His scholarly work includes over fifty publications in international journals, underscoring his significant impact on biomedical engineering and data science.

Short bio: Li Shen, Ph.D., is a Professor of Informatics and Radiology at the Perelman School of Medicine in the University of Pennsylvania. He serves as the Associate Director for Bioinformatics at the Penn Institute for Biomedical Informatics and Co-Director of the Penn Center for AI and Data Science for Integrated Diagnostics. His research interests include medical image computing, biomedical informatics, machine learning, trustworthy AI, NLP/LLMs, network science, imaging genomics, multi-omics, Alzheimer’s disease, and big data science in biomedicine. He has authored over 360 peer-reviewed articles in these fields. His work has been continuously supported by the NIH and NSF. His current research program is focused on developing and applying informatics, computing and trustworthy AI methods for discovering actionable knowledge from complex biomedical and health data (e.g., genetics, omics, imaging, biomarker, outcome, EHR, health care), with applications to complex disorders such as Alzheimer’s disease. Dr. Shen has served on a variety of scientific journal editorial boards, grant review committees, and organizing committees of professional meetings in medical image computing and biomedical informatics. He served as the Executive Director of the Medical Image Computing and Computer Assisted Intervention (MICCAI) Society between 2016 and 2019. He is a fellow of the American Institute for Medical and Biological Engineering (AIMBE), a distinguished member of the Association for Computing Machinery (ACM), and a distinguished contributor of the IEEE Computer Society.

Short bio: Dr. Rusu is an Assistant Professor, in the Department of Radiology, and, by courtesy, Department of Urology and Biomedical Data Science, at Stanford University, where she leads the Personalized Integrative Medicine Laboratory (PIMed). The PIMed Laboratory has a multi-disciplinary direction and focuses on developing analytic methods for biomedical data integration, with a particular interest in radiology-pathology fusion to facilitate radiology image labeling. The radiology-pathology fusion allows the creation of detailed spatial labels, that later on can be used as input for advanced machine learning, such as deep learning. The recent focus of the lab has been on applying deep learning methods to detect and differentiate aggressive from indolent prostate cancers on MRI using the pathology information (both labels and the image content), work that was recently published in Medical Physics and Medical Image Analysis Journals. Moreover, our project are interested in further develop these approaches for ultrasound images.

Program Committee Members

Andrea Lara, Galileo University, Guatemala

Atefe Hassani, Shahed University, Iran

Gang Li, University of North Carolina, USA

Ilwoo Lyu, Pohang University of Science and Technology, South 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

Mayssa Soussia, Imperial College London, UK

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

Philip Chikontwe, Harvard University, USA

Qian Tao, Delft University of Technology, Netherlands

Qingyu Zhao, Weill Cornell Medicine, USA

Reza Azad, RWTH University, Germany

Seung Yeon Shin, Hanyang University ERICA, South Korea

Soochahn Lee, Kookmin University, South Korea

Su Ruan, Laboratoire LITIS, France

Won-ki Jeong, Korea University, South Korea

Yalin Wang, Arizona State University, USA

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/PRIME2024

Key dates:

Full Paper Deadline: June 24, 2024; 11:59 PM EST; extended to July 1, 2024 at 11:59 PM EST

Notification of Acceptance: July 15, 2024

Camera-ready Version: July 24, 2024, 11:59 PM PST

Workshop date: October 6, 2024 (PM)

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 2024: 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

Instructions for submitting your camera-ready paper:

Please make sure to upload a zip file including the following documents:
1. The final camera-ready PDF of your paper.
2. All original files are required to generate the final PDF. If you are using Latex, please make sure to include .tex, .bib, figures, and any other files for compiling the tex file. If you are using Word, please upload the docx file. Name the main file using your submission ID number (example: 32.tex or 32.docx).
3. Please download and fill out the Springer copyright form which can be found at https://shorturl.at/KvCt2 

Include the signed PDF form of the PRIME LNCS Copyright document.

Please make sure that the copyright forms have been filled out correctly before uploading the zip file.

The paper length can vary between 8 and 12.5  pages.

Finally, please upload only one zip (not rar) file with everything included (using your paper submission ID, for example: PRIME-32.zip, where 32 is your paper ID number).

Program & Proceedings

To access the tentative program for PRIME-MICCAI 2024, please click on: PRIME MICCAI 2024 Program