为医疗影像AI落地难题提供轻量化模型与压缩技术方案
Efficient Deep Learning for Medical Imaging: Bridging the Gap Between High-Performance AI and Clinical Deployment
- 聚焦轻量CNN、小规模Transformer和线性复杂度模型三类高效架构
- 验证剪枝、量化等压缩技术可在保持诊断性能前提下降低算力需求
- 适合关注AI临床部署的医生、工程师及算法研发人员
深度学习已彻底改变医学影像分析，广泛应用于现代临床。然而，大规模模型在真实临床环境中部署仍面临高计算成本、延迟限制及云端处理带来的患者数据隐私问题。为此，本文系统综述了专为医疗领域设计的高效轻量化深度学习架构。将当前高效模型分为三类：卷积神经网络（CNN）、轻量级Transformer与新兴的线性复杂度模型。同时，分析剪枝、量化、知识蒸馏和低秩分解等关键模型压缩策略，评估其在维持诊断性能的同时降低硬件要求的有效性。通过揭示现有局限并探讨向设备端智能演进的趋势，本综述为研究人员和从业者提供了从高性能AI迈向资源受限临床环境的实施路径。
原文摘要 · Abstract (English)
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.
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