ADSMI @ MICCAI 2024

MICCAI Workshop on Advancing Data Solutions in Medical Imaging AI

ADSMI: MICCAI Workshop on Advancing Data Solutions in Medical Imaging AI

In the rapidly evolving field of Medical Imaging AI, the quest for robust, fair, and efficient computational models is more critical than ever. With the advent of advanced imaging techniques and the exponential growth in the volume and complexity of medical data, traditional machine learning approaches face significant hurdles in data solutions. The MICCAI Workshop on Advancing Data Solutions in Medical Imaging AI (ADSMI) aims to provide a venue for discussions and innovations at the confluence of medical imaging, artificial intelligence, and data science, addressing the nuanced challenges of data scarcity, quality, and interoperability in medical imaging AI.

ADSMI is the joint workshop of Data Augmentation, Labeling, and Imperfections (DALI), Big Task Small Data, 1001-AI (BTSD-1001AI), and Medical Image Learning with Limited and Noisy Data (MILLanD).

Important Dates

  • Paper Submission Opens: April 18, 2024
  • Paper Submission Deadline: June 24, 2024
  • Notification to Authors: July 15, 2024
  • Camera Ready Deadline: August 2, 2024
  • Workshop Day: October 6, 2024, Marrakesh, Morocco

Call For Papers

At a critical juncture in medical imaging AI, the integration of expansive datasets, foundational models, and advanced methodologies holds the promise of transforming diagnosis, treatment planning, and patient care. Yet, this potential is frequently curtailed by inherent data-related challenges, including limited annotations, privacy issues, and the long-tail distribution of medical conditions. Furthermore, the ethical dimensions of AI—encompassing fairness, equity, and bias—demand a shift towards more inclusive, transparent, and accountable AI systems.

ADSMI is committed to nurturing a multidisciplinary dialogue that narrows the gap between technological progress and clinical application. Our agenda covers a wide array of topics, ranging from novel data synthesis methods to the compilation of comprehensive, multimodal datasets mirroring patient population diversity. We delve into the exploration of foundational model training, utilizing the capabilities of large neural networks to explore new potentials in medical image analysis, diagnosis, and tailored medicine.

The workshop also seeks contributions that advance data curation practices, algorithmic fairness, privacy protection techniques, and the establishment of benchmarks to enable fair comparisons among emerging technologies. Special interest is directed towards innovative solutions for handling noisy or incomplete data and annotations, domain adaptation, incorporation of domain-specific knowledge within AI models, and foundation model development and applications. Our objective is to foster the development of advanced AI solutions that are also practically useful and in alignment with the goals of public health and patient welfare.

We extend an invitation to researchers, clinicians, technologists, and industry stakeholders to share their insights, research findings, and future visions for data solutions in medical imaging AI. Through keynote talks, paper presentations, and interactive sessions, ADSMI endeavors to stimulate the development of AI solutions that are technologically sound, ethically grounded, and universally accessible. Our topics of interest include but not limited to:

  • Innovations in semi-, weakly-, and self-supervised learning.
  • Multi-modal and synthetic data utilization.
  • Domain adaptation, generalization, and learning from limited datasets.
  • Enhancements in data annotation efficiency and strategies for managing label imperfections.
  • Development and evaluation of new datasets aligned with the workshop’s themes.
  • Foundation model development and applications for medical image analysis.
  • Exploration of zero-shot learning in medical image analysis for recognition, detection, and segmentation.
  • Efficient annotation methodologies employing one-shot or few-shot learning.
  • Techniques for synthesizing medical images across diverse modalities, imaging sites, and from textual descriptions.
  • Ethical considerations in AI-driven medical imaging, focusing on fairness, equity, and privacy.

Submissions to our workshop will be managed using the same platform as the main MICCAI conference, using Microsoft CMT. Workshop paper submission website is at: https://cmt3.research.microsoft.com/ADSMI2024

The ADSMI workshop will employ the same reviewing standards as the main conference. ADSMI workshop paper submissions should be anonymized to accommodate a double-blind review. Papers should be formatted using LaTeX or MS Word templates available at Lecture Notes in Computer Science. Manuscripts should be up to 8 pages (text, figures, and tables) plus up to 2 pages of references. In submitting a paper, authors implicitly acknowledge that no paper of substantially similar content has been or will be submitted to another conference or workshop until the decisions have been made by our workshop. Supplemental material submission is optional, which may include:

  • Videos of results that cannot be included in the main paper
  • Anonymized related submissions to other conferences and journals
  • Appendices or technical reports containing extended proofs and mathematical derivations that are not essential for the understanding of the paper

Contents of the supplemental material should be referred to appropriately in the paper, and reviewers are not obliged to look at it.

Program

Keynote Speakers

  • TBD

People

General Chairs:

  • Yuan Xue, Ohio State University, USA
  • Chen Chen, University of Sheffield, UK
  • S. Kevin Zhou, University of Science & Technology of China, China
  • Rama Chellappa, Johns Hopkins University, USA
  • Sameer Antani, National Institutes of Health, USA
  • Zhiyun Xue, National Institutes of Health, USA

Executive Chairs

  • Chao Chen, Stony Brook University, USA
  • Esther Puyol-Antón, King’s College London, UK
  • Le Zhang, University of Birmingham, UK
  • Bo Zhou, Northwestern University, USA
  • Xueqi Guo, Yale University and Siemens Healthineers, USA
  • Marleen de Bruijne, University Medical Center Rotterdam, Netherlands and the University of Copenhagen, Denmark
  • Qingsong Yao, Chinese Academy of Science, China
  • Sivaramakrishnan Rajaraman, National Institutes of Health, USA
  • Zhaohui Liang, National Institutes of Health, USA
  • Ghada Zamzmi, Food and Drug Administration, USA
  • Marius George Linguraru, Children’s National Hospital, USA

Program Committee

TBD