Summary

The EMERGE workshop is an exciting new event at the MICCAI 2024 conference, offering a platform for early-career researchers within the MICCAI community to showcase their work. The workshop is tailored to projects where students have a leading role in the research project, encouraging early-career researchers as the main researchers. This workshop is a student-led initiative, organized by the MICCAI Student Board, with a panel of experienced researchers from the MICCAI community. The workshop is an opportunity for students to get feedback from our experienced panel, who will actively engage with student presentations at the workshop. This student-led initiative reflects a commitment to empowering the next generation of researchers, providing them with an opportunity to present and discuss their ongoing projects - fostering constructive feedback and meaningful scientific discourse. Our workshop aims to highlight works from students from around the world, with a special focus on those in underrepresented regions, including Morocco and other African nations.

The scope of the workshop spans the areas of Medical Image Computing (MIC) and Computer-Assisted Interventions (CAI), aligned with the scientific focus of the MICCAI Society. We encourage the development and implementation of advanced algorithms to solve a variety of problems in medical imaging, encompassing a spectrum of imaging modalities, including MRI, ultrasound, X-ray, microscopy, OCT, nuclear medicine, among others. The event is welcome to students at the undergraduate, master's, and doctoral levels - with an emphasis on works where the student is the primary contributor.

The EMERGE workshop will be held on 6 October 2024 as an in-person, half day satellite event of MICCAI 2024 at the Palmeraie Conference Centre in Marrakesh, Morocco. This in-person format allows for early-stage research discussions and student networking, providing an environment conducive to meaningful exchanges and collaborations. The workshop will conclude with an awards ceremony recognizing outstanding presentations and research works. In addition to these sessions, two successful early-career independent researchers will deliver keynote speeches, offering insights into career development paths within the field and an education-focused talk on effective approaches to impactful research.

Submission

Submissions are double-blind reviewed and must adhere to the following guidelines: maximum paper length: 8 pages (text, figures, and tables) and additional 2 pages allowed for references. Details on the template for the paper can be found here . Accepted papers will be published in an LNCS volume by Springer Nature.

The manuscripts can be submitted at https://openreview.net/group?id=MICCAI.org/2024/Workshop/MSB.

Instructions for Camera-Ready Submission

Please submit a PDF version of your paper. Additionally, you will need to submit the following doucments: (i) source files (LaTeX, Word, etc.), (ii) duly signed License to Publish document by one of the authors, and (iii) a separate pdf document outlining the changes made to your paper based on reviewer feedback.

Camera-Ready Submission Guidelines

  1. Page Limit: The submitted manuscript should be a maximum of 9 pages, plus an additional 2 pages for references. The overall paper length must not exceed 11 pages (including appendix and references).
  2. Paper Number: Please use your two-digit OpenReview submission number for your paper (e.g., use "01" for submission number 1).
  3. License to Publish: You can find the appropriate form on: https://www.springer.com/gp/computer-science/lncs/conference-proceedings-guidelines. Please be sure to use the following title for the proceedings when completing the License to Publish form: "Medical Information Computing, MImA 2024 + MSB EMERGE 2024 (MICCAI Workshops)".
  4. ORCID IDs: We request all authors to include their ORCID IDs in the publication. You can find more information about ORCID IDs here: http://bit.ly/2H5xBpN.

Please update your submission on OpenReview by August 30th, 2024. You can do this by editing your original submission and selecting "Camera Ready Version." The system will allow you to update the following fields: Title, Keywords, TL;DR, Abstract, and Camera-Ready ZIP submission (containing your paper, source code, license, and changes document).

Deadlines

All dates are in 23:59 Pacific Time Zone (PT)

May 6, 2024 Call for Papers
June 1, 2024 Submission Portal Open
June 24, 2024
July 1, 2024		 Paper Submission Deadline
July 9, 2024 Reviews released to the authors
July 13, 2024 Rebuttals due
July 15, 2024 Final Decision
August 30, 2024 Camera Ready Submission

Speakers

Mathias Unberath

Mathias Unberath is the John C. Malone Associate Professor in the Department of Computer Science with secondary appointments in the Departments of Ophthalmology and Otolaryngology—Head and Neck Surgery at the School of Medicine. He is also a core faculty member of the Laboratory for Computational Sensing and Robotics (LCSR) and the Malone Center for Engineering in Healthcare and an affiliate faculty member in the Institute for Assured Autonomy.

With his group, the Advanced Robotics and Computationally AugmenteD Environments (ARCADE) Lab, Unberath builds the future of computer-assisted medicine. Through synergistic research on imaging, computer vision, machine learning, and interaction design, he invents human-centered solutions that are embodied in emerging technologies such as mixed reality and robotics.

He has published more than 150 journal and conference articles and has received numerous awards, grants, and fellowships, including the National Institute of Biomedical Imaging and Bioengineering Trailblazer R21 Award, an NSF CAREER Award, a Google Research Scholar Award, a Johns Hopkins Career Impact Award, and an inaugural Johns Hopkins Data Science and AI Institute Junior Faculty Award.

While completing a bachelor’s in physics and master’s in optical technologies at the Friedrich-Alexander University of Erlangen-Nürnberg (FAU), Unberath also studied at the University of Eastern Finland as an Erasmus Mundus scholar in 2011 and joined Stanford University as a DLR-DAAD fellow in 2014. He received his PhD in computer science from FAU and graduated summa cum laude in 2017. Prior to joining as faculty, Unberath was an assistant research professor in the department and a postdoctoral fellow at LCSR.

Schedule

Time Speaker and Title
08:00 - 08:15 Opening Remarks and Introduction
08:15 - 08:35 1: Non-Parametric Neighborhood Test-Time Generalization: Application to Medical Image Classification
08:35 - 08:55 2: Client Security Alone Fails in Federated Learning: 2D and 3D Attack Insights
08:55 - 09:15 3: Context-Guided Medical Visual Question Answering
09:15 - 10:00 Keynote Talk: Mathias Unberath
10:00 - 10:30 Break
10:30 - 10:50 4: Self-consistent deep approximation of retinal traits for robust and highly efficient vascular phenotyping of retinal colour fundus images
10:50 - 11:10 5: GRAM: Graph Regularizable Assessment Metric
11:10 - 11:30 6: Unsupervised Analysis of Alzheimer’s Disease Signatures using 3D Deformable Autoencoders
11:30 - 11:50 7: Deep Feature Fusion Framework for Alzheimer’s Disease Staging using Neuroimaging Modalities
11:50 - 12:10 8: Explainable Few-Shot Learning for Multiple Sclerosis Detection in Low-Data Regime
12:10 - 12:30 Closing Remarks and Awards

Organization

Naren Akash

IIIT Hyderabad

Website

Moritz Fuchs

Technical University of Darmstadt

Website

Amar Kumar

McGill University

Website

Ahmed Nebli

Forschungszentrum Juelich

Website

Anna Zapaishchykova

Brigham and Women's Hospital

Website

Yanis Najy Miracoui

Standford University

Website

Weina Jin

Simon Fraser University

Website

Harry Anthony

University of Oxford

Website

Amin Ranem

Technical University of Darmstadt

Website

Advaith Veturi

University of Colorado Anschutz Medical Campus

Website

Paul Wilson

Queen's University

Website

Benjamin Killeen

Johns Hopkins University

Website

Constantin Ulrich

German cancer Research Center (DKFZ)

Website

Camila Gonzalez

Stanford University

Website

Antonio R. Porras

University of Colorado Anschutz Medical Campus

Website

Anees Kazi

Massachusetts General Hospital, Harvard Medical School

Website

Program Committee

Aisha Urooj, Mayo Clinic, USA

Arman Gorji, Hamadan University of Medical Science, Iran

Balamurali Murugesan, ETS Montreal, Canada

Berardino Barile, McGill University, Canada

Camila Gonzalez, Stanford University, USA

Constantin Ulrich, German Cancer Research Center, Germany

Divyanshu Tak, Harvard Medical School, USA

Fabian Gröger, University of Basel, Switzerland

Fahad Shamshad, Mohamed Bin Zayed University of Artificial Intelligence, UAE

Favour Nerrise, Stanford University, USA

Harry Anthony, University of Oxford, UK

Henry John Krumb, TU Darmstadt, Germany

Kumar Abhishek, Simon Fraser University, Canada

Magdalini Paschali, Stanford University, USA

Mirko Konstantin, TU Darmstadt, Germany

Raghav Mehta, Imperial College London, UK

Roa'a Al-Emaryeen, University of Jordan, Jordan

Roger David Soberanis-Mukul, Johns Hopkins University, USA

S. Shailja, Stanford University, USA

Valentina Corbetta, Netherlands Cancer Institute, Netherlands

Weina Jin, Simon Fraser University, Canada

Yanis Najy Miraoui, Stanford University, USA

Yuhan Wang, Kings College London, UK

Ziyun Liang, University of Oxford, UK