Deep Learning in Medical Imaging: Architectures, Clinical Applications, and Emerging Directions
TL;DR
Major deep learning architectures, including CNNs, residual networks, UNet, attention-based models, Vision Transformers, and hybrid approaches, along with their clinical applications are summarized and emerging directions such as self-supervised learning, Explainable AI, federated learning, and lightweight models are highlighted as promising approaches for more reliable and accessible medical image analysis.
Abstract
Medical image analysis has evolved from manually designed image features to deep learning methods that can learn useful patterns directly from medical scans. These approaches have shown strong potential in tasks such as disease classification, lesion detection, and image segmentation across modalities including MRI, CT, X-ray, Ultrasound, retinal imaging, and dermoscopy. This review summarizes major deep learning architectures, including CNNs, residual networks, UNet, attention-based models, Vision Transformers, and hybrid approaches, along with their clinical applications. It also discusses key barriers to practical adoption, such as limited annotated data, differences between imaging systems, model interpretability, privacy concerns, and computational requirements. Finally, emerging directions such as self-supervised learning, Explainable AI, federated learning, and lightweight models are highlighted as promising approaches for more reliable and accessible medical image analysis.