Self-Supervised Learning for Multimodal Precision Oncology: Unlocking Unlabeled Clinical and Imaging Data

Abstract

Cancer remains one of the leading causes of morbidity and mortality worldwide despite remarkable advances in molecular diagnostics, targeted therapeutics, immunotherapy, and precision medicine. Modern oncology continuously generates enormous quantities of heterogeneous biomedical data through radiological imaging, digital pathology, genomic sequencing, transcriptomics, proteomics, metabolomics, laboratory investigations, electronic health records, wearable technologies, and longitudinal clinical documentation. Although these datasets possess tremendous potential for advancing precision oncology, only a small proportion is manually annotated, creating a major limitation for conventional supervised artificial intelligence (AI). Self-supervised learning (SSL) has emerged as a transformative machine learning paradigm that enables models to learn generalized biomedical representations directly from unlabeled data without requiring extensive manual annotation. By leveraging pretext tasks, contrastive learning, masked modeling, multimodal representation learning, transformer architectures, graph neural networks, and foundation models, SSL has significantly improved computational oncology across medical imaging, digital pathology, radiogenomics, computational pathology, multi-omics integration, clinical decision support, and personalized therapeutics. Furthermore, SSL facilitates transfer learning, few-shot learning, multimodal foundation models, digital twins, and agentic AI, thereby accelerating development of generalized biomedical intelligence for precision medicine. Despite remarkable advances, important challenges remain regarding multimodal data harmonization, computational scalability, interpretability, regulatory validation, and clinical translation. This review provides a comprehensive overview of self-supervised learning in multimodal precision oncology, highlighting computational foundations, current applications, implementation challenges, and future perspectives for unlocking the full potential of unlabeled biomedical data.[1]