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 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.


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.


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.


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.

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

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.

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.