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This review paper synthesizes efficient deep learning architectures for medical imaging, addressing the challenges of deploying large models in resource-constrained clinical settings. It categorizes models into CNNs, Lightweight Transformers, and Linear Complexity Models, and examines compression strategies like pruning and quantization. The review aims to guide researchers in developing on-device intelligence solutions that maintain diagnostic performance with reduced hardware requirements.
Model compression techniques can make cutting-edge medical imaging AI practical for real-world clinical deployment, even with limited resources.
Deep learning has revolutionized medical image analysis, playing a vital role in modern clinical applications. However, the deployment of large-scale models in real-world clinical settings remains challenging due to high computational costs, latency constraints, and patient data privacy concerns associated with cloud-based processing. To address these bottlenecks, this review provides a comprehensive synthesis of efficient and lightweight deep learning architectures specifically tailored for the medical domain. We categorize the landscape of modern efficient models into three primary streams: Convolutional Neural Networks (CNNs), Lightweight Transformers, and emerging Linear Complexity Models. Furthermore, we examine key model compression strategies (including pruning, quantization, knowledge distillation, and low-rank factorization) and evaluate their efficacy in maintaining diagnostic performance while reducing hardware requirements. By identifying current limitations and discussing the transition toward on-device intelligence, this review serves as a roadmap for researchers and practitioners aiming to bridge the gap between high-performance AI and resource-constrained clinical environments.
