Refreshments are available starting at 10:30 a.m. The seminar will begin at 10:45 a.m.
Abstract
Cancer, a leading cause of death, can be effectively treated if detected in its early stages. However, early detection is difficult for both humans and computers. AI can identify details beyond human perception, delineate anatomical structures, and localize abnormalities in medical images, but achieving this level of reliability requires “AI-ready” datasets—big datasets with carefully prepared annotations—and resources that are often limited and expensive in medical imaging. Several disciplines—particularly the success of GPTs—have shown the transformative power of scaling laws for AI advancement, but this concept remains relatively under-explored in medical imaging. This talk will discuss how scaling AI-ready datasets can positively impact new methodologies and applications in medical imaging, with a special focus on enabling earlier cancer detection.
Speaker Biography
Zongwei Zhou is an incoming assistant research professor in the Department of Computer Science at the Johns Hopkins University and a member of the Malone Center for Engineering in Healthcare. His research focuses on medical computer vision, language, and graphics for cancer detection and diagnosis. He is best known for developing UNet++, a widely adopted segmentation architecture cited nearly 15,000 times since its publication in 2019. He currently serves as a principal investigator on a $2.8 million R01 grant from the National Institutes of Health and the National Institute of Biomedical Imaging and Bioengineering. Zhou’s work has earned multiple honors, including a Doctoral Dissertation Award from the American Medical Informatics Association, an Elsevier Medical Image Analysis Best Paper Award, and a Young Scientist Award from the Medical Image Computing and Computer Assisted Intervention Society. He also received the Arizona State University President’s Award for Innovation, the highest honor for graduate students at ASU, and has been recognized among Stanford University’s World’s Top 2% Scientists every year since 2022.