Special Sessions

Thursday | April 9, 2026 | 15:00 – 16:00

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.

Invited Talk: Setting the stage: why data preparation is the bottleneck for medical imaging foundation models, Martin J. Willemink (Segmed)
This talk will elaborate on best practices, challenges, and innovations in preparing medical data for foundation model development, from raw clinical datasets to curated, high-quality inputs.

Thursday | April 9, 2026 | 16:30 – 17:30

Organizers: Irina Voiculescu (University of Oxford), Vito Paolo Pastore (University of Genoa)

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.

Invited Talk: Counterfactual Contrastive Analysis: Learning Common and Salient Generative Factors Between Two Image Sets, Pietro Gori (LTCI, Télécom Paris, Institut Polytechnique de Paris)
A model-agnostic framework for generating visual counterfactual explanations with contrastive analysis.

Invited Talk: The Map Exists. Let’s Build it: Closing the Gap Between Trustworthy AI Principles and Practice, Maria A. Zuluaga (Data Science Department, EURECOM, Biot, France)
Closing the gap between agreed principles and real-world implementation of trustworthy AI in medical imaging.

Friday | April 10, 2026 | 8:00 – 9:00

Organizers: Valentina Giannini (University of Turin), Samanta Rosati (Politecnico di Torino) and Giulia Nicoletti (University of Turin)

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.

Invited Talk: Precision Oncology Digital Twins: Integrating Multimodal Data for Next-Generation Care, Valentina Giannini (University of Turin)
An overview of methods for multimodal data aggregation in digital twin design, with a focus on enabling therapy de-escalation in breast cancer.

Friday | April 10, 2026 | 10:30 – 11:30

Organizers: Raffaele Mineo (University Campus Bio-Medico of Rome), Simone Palazzo (University of Catania), Giovanni Bellitto (University of Catania), Amelia Sorrenti (University Campus Bio-Medico of Rome), Federica Proietto Salanitri (University of Catania), Concetto Spampinato (University of Catania)

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.

Invited Talk: Trustworthy Continual Learning for Medical Image Analysis, Xiaofeng Liu (Yale University)
A talk on building trustworthy medical imaging AI that can continuously adapt to shifting data, new tasks, and privacy constraints while remaining reliable, secure, and clinically effective.