Medical imaging is a key component in clinical diagnosis, treatment planning and clinical trial design, accounting for almost 90% of all healthcare data. CNNs achieved performance gains in medical image analysis (MIA) over the last years. CNNs can efficiently model local pixel interactions and be trained on small-scale MI data. The main disadvantage of typical CNN models is that they ignore global pixel relationships within images, which limits their generalisation ability to understand out-of-distribution data with different 'global' information. The recent progress of Artificial Intelligence gave rise to Transformers, which can learn global relationships from data. However, full Transformer models need to be trained on large-scale data and involve tremendous computational complexity. Attention and Transformer compartments (Transf/Attention) which can well maintain properties for modelling global relationships, have been proposed as lighter alternatives of full Transformers. Recently, there is an increasing trend to co-pollinate complementary local-global properties from CNN and Transf/Attention architectures, which led to a new era of hybrid models. The past years have witnessed substantial growth in hybrid CNN-Transf/Attention models across diverse MIA problems. In this systematic review, we survey existing hybrid CNN-Transf/Attention models, review and unravel key architectural designs, analyse breakthroughs, and evaluate current and future opportunities as well as challenges. We also introduced a comprehensive analysis framework on generalisation opportunities of scientific and clinical impact, based on which new data-driven domain generalisation and adaptation methods can be stimulated.

翻译：医学影像在临床诊断、治疗方案制定和临床试验设计中占据核心地位，约占所有医疗数据的90%。近年来，卷积神经网络在医学图像分析领域取得了性能提升。CNN能够高效建模局部像素交互，并可在小规模医学影像数据上训练。典型CNN模型的主要缺陷在于其忽视了图像内部的全局像素关系，这限制了其理解具有不同"全局"信息的分布外数据的泛化能力。人工智能的最新进展催生了Transformer，该类模型能够从数据中学习全局关系。然而，完整Transformer模型需要在大规模数据上训练，且计算复杂度极高。为保持全局关系建模特性，注意力机制和Transformer组件（Transf/Attention）作为完整Transformer的轻量化替代方案被提出。近期，融合CNN与Transf/Attention架构的局部-全局互补特性成为趋势，催生了混合模型的新纪元。过去数年见证了混合CNN-Transf/Attention模型在各类MIA问题中的显著增长。本系统综述对现有混合CNN-Transf/Attention模型进行调研，梳理并剖析关键架构设计，分析突破性进展，评估当前及未来机遇与挑战。我们还引入了一个面向科学及临床影响泛化能力的综合分析框架，以此可推动基于数据驱动的领域泛化与自适应新方法的发展。