Abstract

Early tumor detection saves lives. Each year, more than 300 million computed tomography (CT) scans are performed worldwide, offering a vast opportunity for effective cancer screening. However, detecting small or early-stage tumors on these CT scans remains challenging, even for experts. Artificial intelligence (AI) models can assist by highlighting suspicious regions, but training such models typically requires extensive tumor masks–detailed, voxel-wise outlines of tumors manually drawn by radiologists. Drawing these masks is costly, requiring years of effort and millions of dollars. In contrast, nearly every CT scan in clinical practice is already accompanied by medical reports describing the tumor’s size, number, appearance, and sometimes, pathology results–information that is rich, abundant, and often underutilized for AI training. This talk will introduce ways of training AI to segment tumors that match their descriptions in medical reports. This approach scales AI training with large collections of readily available medical reports, substantially reducing the need for manually drawn tumor masks.

Bio

Zongwei Zhou is an Assistant Research Professor in the Department of Computer Science at 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 16,000 times since its publication in 2019. He currently serves as PI on an NIH–NIBIB R01 grant ($2.8M, top 1.0 percentile). His work has earned multiple honors, including the AMIA Doctoral Dissertation Award, Elsevier–MedIA Best Paper Award, and MICCAI Young Scientist Award. Dr. Zhou also received the President’s Award for Innovation, the highest honor for graduate students at Arizona State University, and has been recognized among the Top 2% of Scientists Worldwide every year since 2022.