Workshops

Preclinical and Clinical Applications of Photoacoustic Imaging

Abstract: This workshop will focus on the preclinical applications and clinical translation of photoacoustic imaging (PAI), a hybrid imaging technique that combines optical and ultrasound modalities to provide high-resolution, functional, and molecular information from deep within biological tissues. The session will explore cutting-edge advancements in PAI and its transformative potential in areas such as cancer detection, vascular imaging, and functional brain imaging. The topic is highly relevant to the IEEE ISBI conference, as it bridges the gap between engineering innovation and biomedical applications, aligning with the conference’s focus on biomedical imaging and signal processing. By emphasizing the translational impact of photoacoustic imaging, this session will provide valuable insights into how cutting-edge imaging technologies can address real-world clinical challenges, attracting interest from researchers, clinicians, and engineers.

Lei S. Li

Rice University, USA

Jun Xia

University of Buffalo SUNY, USA

Foundation AI Models in Biomedical Imaging (FAIBI)

Abstract: Foundation AI models are generalistic AI models that have recently garnered huge attention in the AI research community. Foundation AI models bring scalability and broad applicability and, thus, possess transformative potential in medical imaging applications, including (but not limited to) synthesis of medical image data, automatic report generation from radiology images, cross-lingual report generation, and image analysis. This workshop aims to explore new applications of foundations AI models in biomedical imaging with a focus on multimodal foundation models for multimodality medical data comprising medical images (radiology, pathology, fundus, etc), electronic health records, medical reports, radiomics, etc. Furthermore, the workshop will also provide a platform to identify the practical challenges of implementing foundation AI models in the biomedical imaging domains and the potential solutions related to the robustness, trustworthiness, and explainability of the medical foundation AI models. Thus, the workshop will offer an understanding of the impact of foundation AI models on the biomedical imaging domain. The workshop will comprise keynote presentations by experts, contributed paper presentations, poster sessions, and a panel discussion to encourage knowledge sharing, ideas exchange, and collaboration among the participants.

Hazrat Ali

University of Stirling, United Kingdom (Great Britain)

Rizwan Qureshi

University of Central Florida, USA

Jia Wu

MD Anderson, USA

Islem Rikek

Imperial College London, United Kingdom (Great Britain)

Stephen R Aylward

NVIDIA & The University of North Carolina at Chapel Hill, USA

Open-source MONAI: Next-Generation Capabilities for Biomedical Imaging AI

Abstract: The Medical Open Network for AI (MONAI https://monai.io) Project is an open-source platform with over 3.5 million downloads that is well known for its capabilities for medical image segmentation, classification, and annotation AI research and development involving biomedical images of all types, from MRI and CT to pathology and surgical video. With over 220 contributors from around the world, it contains cutting-edge training methods, metrics, tutorials, and success stores. It also offers leading methods for AI-assisted annotation of biomedical images and for deploying AI models in laboratory and clinical workflows.

In this course we will provide an overview of MONAI’s well-established capabilities, and we will introduce two exciting new capabilities: generative AI for image simulation and vision-language models (VLMs) for medical image co-pilots. We demonstrate how to explore, use, and optimize these new features in biomedical research and product developments. We will also explore how to integrate MONAI with the tools you use every day: 3D Slicer, CVAT, Jupyter Notebooks, and cloud services.