Cancer remains a leading cause of death worldwide, presenting complex challenges that demand innovative solutions. Integrating computer vision, machine learning, and AI into oncology and cancer screening can transform cancer care by enabling earlier detection, more precise prevention strategies, and personalized treatment approaches. This workshop focuses on applying multi-modal foundation models in advancing cancer research and clinical practice. 

Multi-modal foundation models leverage diverse data modalities—such as medical imaging (radiology, histopathology), genomics, proteomics, and electronic health records (EHRs)—to create comprehensive frameworks for understanding cancer biology. Foundation models, pre-trained on large-scale datasets and fine-tuned for specific tasks, offer scalability and adaptability across different cancer types and clinical settings. Together, these approaches enable breakthroughs in early diagnosis, risk stratification, treatment optimization, and outcome prediction.

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Invited Speakers

Organizing Committee

Program Committee

Amara Tariq, Mayo Clinic

Man Luo, Intel Labs

Ulas Bagci, Northwestern University

Chen Chen, University of Central Florida

Anthony Bilic, University of Central Florida