NIH Session: “Funding Opportunities and Grant Writing Tips”
Abstract: This session will highlight funding opportunities at the National Institute of Biomedical Imaging and Bioengineering (NIBIB) and provide valuable insights for both new and established investigators aiming to apply to funding. Experts from NIBIB will share strategies for navigating the NIH grant system, preparing competitive applications, and introduce their programs and funding opportunities. Join this interactive session to enhance your grant-writing skills and discover resources to support your pursuit of research funding. The session will also highlight the new Precision Medicine with AI: Integrating Imaging with Multimodal Data (PRIMED-AI) initiative, as well as other trans-NIH funding opportunities. The session will include a Q&A segment where you can ask questions and interact with the panel members.
Preparing Large-Scale Medical Imaging Data for Foundation Model Development
Abstract: Foundation models have gained substantial interest in the medical imaging field. While the potential opportunities are substantial, foundation models require large amounts of data which is a challenge that needs to be addressed for proper development. This special session explores the technical, methodological, and ethical challenges of preparing large-scale medical imaging datasets for the development and validation of foundation models. Topics of interest include strategies for multi-institutional data integration, privacy-preserving de-identification, and scalable data search and curation pipelines. The session also seeks to address how dataset design influences model generalizability, fairness, and reproducibility, as well as the governance frameworks needed to ensure responsible use of imaging data. The goal is that participants will gain practical insights and understand how to shape emerging best practices for large-scale medical AI development.
Safety and Reliability in Medical Imaging
Abstract: The ISBI 2026 special session `Safety and Reliability of Medical Imaging Technology’ will explore challenges and solutions to building trustworthy AI systems for biomedical imaging. Ensuring the fairness, safety, and robustness of clinic-facing AI has become critical. We invite novel research contributions on topics such as bias and fairness, out-of-distribution and failure detection, uncertainty quantification, or the use of explainability in biomedical applications.
Digital Twins and Multi-Omics Integration: Methodological Advances for Personalized Biomedical Modeling
Abstract: This session will explore emerging methodologies for integrating multi-omics, imaging, and clinical data to build digital twins that can simulate individual disease trajectories and therapeutic responses. By convening experts in bioinformatics, radiomics, systems biology, and AI, the session aims to promote cross-disciplinary dialogue and identify key computational and translational challenges. Discussions will focus on strategies for data harmonization, model interpretability, and validation pipelines to ensure robustness, reproducibility, and clinical applicability of digital twin frameworks.
Privacy‑Aware, Data‑Efficient AI via Personalized Incremental and Federated Learning in Healthcare
Abstract: This special session brings together recent advances in personalized incremental (continual) learning and federated learning to address two persistent barriers in biomedical imaging: strict data privacy constraints and chronic data scarcity across institutions. We will showcase methods that enable AI models to adapt over time to longitudinal, non-IID, multi-center data without centralizing patient information, while remaining robust to scanner/protocol shifts. The session will feature contributions spanning algorithms, evaluation protocols, and real clinical use cases (CT/MRI, interventional imaging, oncology), highlighting pathways toward deployable, regulation-aware medical AI. Overall, the goal is to articulate a practical blueprint for privacy-preserving, data-efficient imaging AI in real-world healthcare settings.
Data Crimes in Medical Imaging: Pitfalls, Biases, and Mitigation Strategies
Abstract: This special session focuses on the growing problem of “data crimes” – scenarios where developers of artificial intelligence (AI) models naively use public datasets for tasks they were never designed to support, leading to biased outcomes and misleading algorithmic performance. Focusing on image reconstruction from MRI measurements, we will demonstrate that such workflows, along with hidden data preprocessing steps, can artificially inflate results by up to 40% and jeopardize clinical applicability. Next, we will provide hands-on demonstrations with open-access Python tools, where participants will learn to detect, quantify, and mitigate these sources of bias. The session equips the ISBI community with practical frameworks and guidelines for conducting transparent, reproducible, and trustworthy medical AI research.