Keynotes

Rice University, USA

Monday | April 14, 2025 | 18:00 – 19:00

Abstract: Cancer is the first or second leading cause of premature death in 134 of 183 countries, and it is estimated that global incidence of cancer will increase by 50% from 2018 to 2040. The number of cases is projected to double in countries with low Human Development Index; these countries have the least resources and infrastructure to adequately care for cancer patients. Disparities exist within countries; in the US, racial and ethnic minorities and other medically underserved populations share a disproportionate burden for many types of cancer. Most cancers can be cured if detected early and treated effectively. To reduce premature death, the World Health Organization recommends implementing early cancer detection and prevention programs at the primary care level. Yet, existing tests for early cancer detection are too complex and/or expensive to implement in primary care settings, particularly in medically underserved areas.

This talk will describe the development and deployment of affordable, accurate technologies that integrate imaging and biomarkers for use in low-resource settings. Examples will include systems for early detection of precancerous lesions and for definitive histopathologic diagnosis of cancer.

Even when affordable diagnostic technologies exist, there are many obstacles that must be overcome to deliver them at scale. With few exceptions, low-cost diagnostics targeted for use in low-resource settings are not available for commercial purchase. This is a significant barrier to achieving the promise of affordable technologies to reduce global health inequities. This talk will also highlight how bioengineers can understand and address the needs of low-resource settings, both from an infrastructure and a clinical perspective and how they can access commercial markets in low-resource settings.

The development of simple, effective instrumentation that meets the economic reality of countries should motivate bioengineers and others to see the problem of producing cheaper and more efficient systems, suitable for all regions of the planet, as a natural challenge within their profession.

Biography: Rebecca Richards-Kortum is the Malcolm Gillis University Professor and member of the Department of Bioengineering at Rice University. After receiving a B.S. in Physics and Mathematics from the University of Nebraska-Lincoln in 1985, she continued her graduate work at the Massachusetts Institute of Technology, where she received a PhD in Medical Physics in 1990. She joined the faculty in Bioengineering at Rice University in 2005 and served as Chair of Bioengineering from 2005-2008 and 2012-2014.

She was named a Howard Hughes Medical Institute Professor in 2002 and 2006, and is an elected member of the US National Academy of Engineering, US National Academy of Sciences, the US National Academy of Inventors, the American Academy of Arts and Sciences, and the American Philosophical Society. She is a recipient of a MacArthur Foundation Fellowship.

Dr. Richards-Kortum’s group is developing imaging systems to enable better screening for oral, esophageal, and cervical cancer at the point-of-care in low-resource settings; novel, low-cost sensors to detect infectious diseases at the point-of-care; and technologies to improve neonatal care in low-resource settings.

MD Anderson Cancer Center, USA

Tuesday | April 15, 2025 | 8:00 – 9:00

Abstract: The dramatic growth in digitized measurement, computational capacity, and new approaches to fuse recent data with past data to generate insights is transforming society. Many see this as the emergence of artificial intelligence (AI), but the impact is much broader and the opportunities to transform medicine and image-guided oncology is profound. The current paradigm of image-generation to inform human decision-making is very limited in that it doesn’t fully utilize prior data, isn’t optimized for the decision, and fails to extend the data utility through predictive capacity. There are multiple emerging examples of a more comprehensive and intuitive approach to image-guidance. These range from adaptive radiation therapy through to synthetic image generation and digital twins. Critically, this transition will also put additional pressure on the importance of imaging as a measurement capability versus a picture-taking activity. The presentation will highlight several emerging opportunities for research and technological developments that will allow the field to transition from image guidance to a prediction-guided oncology.

Biography: Dr. David A. Jaffray is a senior vice president and chief technology and digital officer (CTDO) and a Full Professor in Radiation Physics and Imaging Physics at The University of Texas MD Anderson Cancer Center. Before joining MD Anderson, Dr. Jaffray served as executive vice president for Technology and Innovation at the University Health Network (UHN) in Toronto. He designed and led UHN’s digital transformation. In the 17 years there, he also served as Head of Medical Physics, vice chair of Research for the University of Toronto’s Department of Radiation Oncology, founding director of the STTARR Innovation Centre, and founding director of the Techna Institute. He was a Full Professor in the Departments of Radiation Oncology, Medical Biophysics, and IBBME at the University of Toronto and was active in strategic planning, teaching, and graduate student supervision.

Dr. Jaffray holds 28 patents and has authored >300 peer-reviewed publications in topics related to cancer, including, the development of new radiation treatment machines, exploring the fundamental limits of imaging system performance, the development of novel nanoparticle formulations for improved detection of cancer, and challenges in global health.

He has received many honors, including the Sylvia Sorkin-Greenfield Award, the Farrington Daniels Award and the Sylvia Fedoruk Award. In 2018, he received the Gold Medal from the American Society for Radiation Oncology and is now a fellow of the American Association of Medical Physicists (FAAPM), the American Institute for Medical and Biological Engineering (FAIMBE), the Canadian Organization of Medical Physics (COMP), and the US National Academy of Inventors (FNAI). Dr. Jaffray has led the development of a variety of commercial products, including software and hardware for safe, high-quality cancer care and including the development of cone-beam CT guided radiation therapy.

Dr. Jaffray earned his B.Sc. in physics from the University of Alberta, and his Ph.D. in medical biophysics from the University of Western Ontario. He is also Board Certified in the discipline of Medical Physics by the American Board of Medical Physics.

Johns Hopkins University, USA

Tuesday | April 15, 2025 | 14:00 – 15:00

Abstract: Despite impressive advances in computer vision and medical imaging, owing to the reemergence of deep learning/AI methods, two basic questions remain unanswered. Will deep learning/AI methods work everywhere? Will they work for everyone? These two questions can be decomposed into the following problems that need attention, especially in medicine AI. These include domain adaptation and generalization, bias detection and mitigation, the effectiveness of self-supervised learning, the fidelity and veracity of synthetic data, selecting the best subsets of training data, robust methods for handling adversarial attacks, federated learning, and performance guarantees for hierarchical, non-linear regression models,. In this talk, I will discuss these problems and present partial solutions to them.

Biography: Prof. Rama Chellappa is a Bloomberg Distinguished Professor Computer Vision and Artificial Intelligence in the Departments of Electrical and Computer Engineering (ECE) in the Whiting School of Engineering and Biomedical Engineering in the School of Medicine at Johns Hopkins University (JHU). At Hopkins, he is serving as an interim co-Director of the Data Science and Artificial Intelligence Institute, and is affiliated with CIS, CLSP, IAA and MINDS. He also holds the non-tenured position as a College Park Professor in the ECE department at the University of Maryland. His research interests are in artificial intelligence, computer vision, machine learning, medical imaging and pattern recognition. He received the 2012 K. S. Fu Prize from the International Association of Pattern Recognition (IAPR). He is a recipient of the Society, Technical Achievement, and Meritorious Service Awards from the IEEE Signal Processing Society, the Technical Achievement and Meritorious Service Awards from the IEEE Computer Society and the Inaugural Leadership Award from the IEEE Biometrics Council. He received the 2020 IEEE Jack S. Kilby Medal for Signal Processing, the 2023 IEEE Computer Society Pattern Analysis and Machine Intelligence Distinguished Researcher Award, and the 2024 Edwin H. Land Medal from Optica (formerly Optical Society of America). He is a member of the National Academy of Engineering and a Foreign Fellow of the Indian National Academy of Engineering. He has been recognized as a Distinguished Alumni by the ECE department at Purdue University and the Indian Institute of Science. He is a Fellow of AAAI, AAAS, ACM, AIMBE, IAPR, IEEE, NAI, OSA, and the Washington Academy of Sciences and holds nine patents.

University of Texas Southwestern Medical Center, USA

Wednesday | April 16, 2025 | 11:00 – 12:00

Abstract: One often considers precision medicine as a genetic phenomenon, whereby a patient’s genome or epigenome may determine what specific treatment would be of particular benefit to them. However, the genetics merely provides on-the-ground intelligence about the patient. Similarly, precision imaging leverages specific cellular targets, unique to the patient, but also provides spatial information that enables construction of guided missiles that might remove the offending lesions, such as through molecular radiotherapy, also known as “theranostics.” In that context, theranostics are agents that are used both to confirm the presence of target within the patient and then, by switching from the imaging to a therapeutic isotope, treating the target lesions with particulate radiation. We will discuss what is becoming a classic target among theranostics, namely, the prostate-specific membrane antigen (PSMA), and the development of precision imaging and therapeutic agents to leverage it in the treatment of prostate and potentially other cancers. The discussion will serve as an introduction to the field and provide a model for how new agents may be introduced to treat a wide variety of malignancies.

Biography: Martin Pomper is Professor and Chair of Radiology at the University of Texas Southwestern Medical Center. He received undergraduate, graduate (organic chemistry) and medical degrees from the University of Illinois at Urbana-Champaign. Postgraduate medical training was at Johns Hopkins University, including internship on the Osler Medical Service, residencies in diagnostic radiology and nuclear medicine and a fellowship in neuroradiology. His interest is in the development of new imaging and therapeutic agents for cancer, central nervous system disease, and other disorders.

University of Colorado (CU) School of Medicine, USA

Thursday | April 17, 2025 | 11:00 – 12:00

Abstract: AI, particularly deep learning, has the potential to revolutionize healthcare, especially in those areas where imaging plays an important part of the patient’s journey. This talk will showcase recent applications of these technologies to clinical questions in radiology, oncology, and ophthalmology that highlight the promise. Despite these impressive developments, the adoption of such tools in routine clinical practice remains limited. We will explore the challenges of transitioning algorithms from the lab to widespread deployment and discuss strategies to address these barriers.

Biography: Jayashree Kalpathy-Cramer is the endowed chair in Ophthalmic data sciences and the founding chief of the Division of Artificial Medical Intelligence in the Department of Ophthalmology at the University of Colorado (CU) School of Medicine. She is also the Director for Health Informatics at the Colorado Clinical and Translational Sciences Institute. She leads the development and translation of novel artificial intelligence (AI) methods into effective patient care practices at the Sue Anschutz-Rodgers Eye Center. Her research interests span the spectrum from novel algorithm development to clinical development. She is passionate about the potential that machine learning and artificial intelligence have to improve the access and the quality of healthcare in the US and worldwide. She graduated from IIT Bombay, India, with a degree in electrical engineering and received her PhD from Rensselaer Polytechnic Institute, also in the Electrical Engineering. Dr. Kalpathy-Cramer spent almost a decade in the semiconductor industry before a pivot to academia and healthcare. She has co-authored over 250 peer-reviewed publications, has written over a dozen book chapters and is a co-inventor on 15 patents.