14511 Transfer learning for medical image analysis in computerized disease diagnosis

Transfer learning has become an increasingly valuable approach in medical image analysis, particularly in contexts where annotated data are limited and training models from scratch would be computationally prohibitive. This chapter examines the adaptation of pretrained convolutional neural networks and self-supervised learning techniques for a wide range of medical imaging applications, such as disease diagnosis, organ segmentation, and the detection of lesions. Emphasis is placed on practical implementations of transfer learning using established architectures such as VGG16, ResNet, DenseNet, and Inception, as well as on more recent developments in multistage and self-supervised pretraining strategies. In addition, the chapter explores the role of Vision Transformers (ViTs) and hybrid CNN-transformer models, which are emerging as powerful alternatives for extracting contextual and structural information from complex medical images. The chapter reviews the performance of these models across various imaging modalities (e. g., X-ray, CT, MRI, and ultrasound) and evaluates their effectiveness in real-world clinical scenarios. Special attention is paid to multimodal learning, where transfer learning enables the integration of complementary data sources (e. g., imaging, clinical records, and genomic data) to improve diagnostic performance. In addition, case studies are presented to illustrate how transfer learning accelerates deployment in resource-limited healthcare settings by reducing both training time and computational costs. Key challenges, such as domain shifts between natural and medical images, limited generalizability between patient populations, model interpretability, and data privacy constraints, are also discussed in detail. The difficulty of aligning pretrained features with domain-specific representations, particularly when transferring from nonmedical datasets like ImageNet, remains a critical bottleneck. Privacy-preserving approaches, including federated learning and differential privacy, are highlighted as promising strategies to mitigate concerns related to sensitive medical data sharing. By synthesizing current research and real-world case studies, this work highlights both the opportunities and limitations of transfer learning in advancing computer-aided diagnosis. Finally, future directions are outlined, including the development of domain-specific pretraining datasets, the incorporation of explainable AI methods to improve transparency, and the design of lightweight architectures for efficient clinical deployment. Together, these advances aim to make transfer learning-based models more robust, interpretable, and broadly applicable in clinical practice.