Keynote speakers

Invited speakers

Kristy K. BROCK

Kristy Brock has a primary research interest in the interaction of human modeling and radiation therapy. She has investigated the ability of biomechanical models to enhance the veracity of human deformable modeling, and has evaluated the accuracy of deformable alignment methods applied to radiation oncology problems such as contour propagation and dose accumulation. She developed MORFEUS, a comprehensive system for deformable modeling, and lead several investigations in its use on radiation therapy, correlative pathology, and other areas of interest. Over the past few years, she has expanded the biomechanical models to describe anatomical response to radiation, including volume changes and position within the human body.

Irène BUVAT

Irène Buvat is a physicist, specializing in molecular imaging through positron emission tomography (PET). She leads the Translational Imaging Laboratory in Oncology (LITO, U1288 Inserm - Institut Curie) at the Institut Curie. Her research focuses on the development and validation of new biomarkers from PET images to support precision medicine. Irène Buvat is a staunch advocate of reproducible research, and her laboratory, with the help of Christophe Nioche, has developed the free software LIFEx for conducting radiomics studies (high-throughput feature extraction from medical images). Currently, this software is being used by more than 5,000 people worldwide.

Gaël VAROQUAUX

Gaël Varoquaux is a research director working on data science at Inria (French computer science national research) where he leads the Soda team. Varoquaux's research covers fundamentals of artificial intelligence, statistical learning, natural language processing, causal inference, as well as applications to health, with a current focus on public health and epidemiology. He also creates technology: he co-funded scikit-learn, one of the reference machine-learning toolboxes, and helped build various central tools for data analysis in Python.

Harini VEERARAGHAVAN

Developing and deploying trustworthy AI models for cancer treatments

Harini Veeraraghavan is an associate attending computer scientist in the department of medical physics at Memorial Sloan Kettering (MSK) Cancer Center, NY. She is the director of AI for image guided therapies lab at MSK. She leads and directs the clinical translation of AI methods developed by her group for radiotherapy treatment automation. Her research interests are primarily in advancing AI and image analysis solutions for personalized and precision cancer treatments. She leads several projects involving lung and GU/GYN cancers for solving problems related to developing and clinically implementing AI methods for image guided adaptive radiation treatments as well as longitudinal tumor treatment response monitoring. She also develops radiomics and multi-modality integrated AI solutions for prognosticating and predicting cancer treatment response.

Corinne FAIVRE-FINN

Corinne Faivre-Finn is a Professor of Thoracic Radiation Oncology at the University of Manchester and Honorary Consultant Clinical Oncologist. She has numerous professional roles including radiotherapy research lead for Manchester Cancer Research Centre & the Cancer Research UK Lung Cancer Centre of Excellence, Chair of the ESTRO Lung Focus Group and Chair of the Early NSCLC EORTC Lung Group.
She has led numerous trials studying radiotherapy in lung cancer and is an author of international guidelines on the management of patients with lung cancer (ESMO, BTS, EORTC, ESTRO, ERS, ASTRO). In recent years, recognising the limitation of conventional clinical trials, she has developed a keen interest in real word data and pragmatic trials. She leads a programme of research focused on the concept of rapid-learning and an electronic patient reported outcome initiative at her institution.

Gérald GAGLIO

AI in radiology, some lessons coming from the daily practice

Gérald Gaglio is full professor of sociology in the Côte d’Azur University (France). He is a sociologist of innovation. He tries to emphasize the societal issues due to the dissemination and massive use of technologies in our societies. For the past fifteen years, he has been particularly interested in the world of medicine and how it is being challenged by these developments, particularly in the way of work is carried out and how the healthcare relationship evolves. His latest research focuses on the adoption (and non adoption) of AI devices in radiology.

Susanna GUATELLI

Associate Professor Susanna Guatelli is an international leading expert of Monte Carlo radiation transport simulation codes for radiation physics, including medical applications and radiation protection in Earth labs, aviation and space. She is Theme Leader of "Monte Carlo simulations" in the Centre For Medical Radiation Physics, Physics, UOW.

Julien JOMIER

Julien Jomier is a senior software engineering manager at NVIDIA. He leads the developer experience for Holoscan Embedded SDK. Before joining NVIDIA, Julien was director of commercial solutions at Kitware in the US and CEO of Kitware France where he was leading the development of open-source solutions for HPC, computer vision, and medical imaging. Julien has over 20 years of experience in the medical field and was a research lecturer in radiology.

Guillaume LANDRY

AI for motion management

Guillaume Landry is W2 Professor at the Department of Radiation Oncology of the University Hospital of the Ludwig Maximilian University in Munich, Germany. He works on image guidance in radiotherapy, and focusses on the use of deep learning methods. His medical physics research group applies these methods to online adaptive MR-guided radiotherapy and motion management

Natalia SILVIS-CIVIDJIAN

Software-related incidents in RT: What can we learn from them?

Natalia Silvis-Cividjian is assistant professor computer science at the Vrije Universiteit in Amsterdam. During her PhD at the TU Delft she developed Monte Carlo simulations for electron-matter interactions in nanotechnology. For more than ten years already she has been teaching Software Testing with an emphasis on safety-critical systems. She got in touch with the RT field due to Therac-25, a famous computer-controlled linac linked to multiple overexposure accidents in the mid 80's. Recently, together with her students she interviewed a witness of these accidents with the conclusion that even 35 years later, we can still learn from them. As a follow up, she started applying a modern systems-theory based method called STAMP to investigate new incidents occurred at different RT clinics in Europe. The conclusion is that software is a powerful, yet obscure player in the game - it can save lives, but when not engineered properly, it can also contribute to accidents. She leads the VU-BugZoo project that develops tools to teach software testing using a bug-centric approach, and i-SART, a project to build an intelligent digital assistant for safety analysis in RT.

Salim SI-MOHAMED

Spectral CT imaging : principle, applications and perspectives

Salim Si-Mohamed is currently assistant professor at University Claude Bernard Lyon 1 in medical imaging. He is a practicing radiologist working in the imaging department of the cardiovascular and thoracic Louis Pradel hospital in Lyon. He is specialized in diagnostic and interventional imaging of cardiovascular and chest diseases, working mainly with CT technology. His experimental research focuses on the development of spectral photon-counting CT and dual-energy CT systems, in combination with the use of novel contrast agents, for cardiovascular and also oncologic imaging applications.

Jan-Jakob SONKE

AI based reconstruction

Jan-Jakob Sonke is a full professor at the University of Amsterdam and leads a research group at the Netherlands Cancer Institute on adaptive radiotherapy. This group focuses on using medical imaging to quantify anatomical and functional changes and methods to optimally account for such changes. He is the theme leader of image guided therapy research at the Netherlands Cancer Institute and one of the scientific directors of two labs in the Innovation Center for Artificial Intelligence (ICAI): the AI for Oncology lab and the POP-AART lab.

Lei XING

Foundation Models for Medical Imaging and Radiation Oncology

Dr. Xing is currently the Jacob Haimson & Sarah S. Donaldson Professor of Medical Physics and Director of Medical Physics Division of Radiation Oncology Department at Stanford University. He also holds affiliate faculty positions in Department of Electrical engineering and Molecular Imaging Program at Stanford. His research has been focused on AI in medicine, medical imaging, treatment planning, image-guided radiation therapy. He has made unique and significant contributions to each of the above areas. He is a fellow of AAPM, ASTRO, and AIMBE (American Institute for Medical and Biological Engineering). He is the recipient of the 2023 Edith Quimby Lifetime Achievement Award of AAPM.

Xiaofeng YANG

Deep learning in MRI-guided radiation therapy

Xiaofeng Yang, PhD, DABR, is Paul W. Doetsch Associate Professor and serves as Vice Chair for Medical Physics Research in the Department of Radiation Oncology at Emory University School of Medicine. Dr. Yang specializes in image-guided radiotherapy, artificial intelligence, multimodality medical imaging, and medical image analysis. He is the leader of the Deep Biomedical Imaging Laboratory, where he and his team focus on developing AI-aided analytical and computational tools to enhance the role of quantitative imaging in cancer treatment and improve the accuracy and precision of radiation therapy. His current research projects include image-guided radiotherapy, motion tracking using real-time imaging, CBCT-guided adaptive radiotherapy, MRI-only based treatment planning, advanced image analysis algorithm development and clinical applications.