Machine learning has been developed dramatically and witnessed a lot of applications in various fields over the past few years. This boom originated in 2009, when a new model emerged, that is, the deep artificial neural network, which began to surpass other established mature models on some important benchmarks. Later, it was widely used in academia and industry. Ranging from image analysis to natural language processing, it fully exerted its magic and now become the state-of-the-art machine learning models. Deep neural networks have great potential in medical imaging technology, medical data analysis, medical diagnosis and other healthcare issues, and is promoted in both pre-clinical and even clinical stages. In this review, we performed an overview of some new developments and challenges in the application of machine learning to medical image analysis, with a special focus on deep learning in photoacoustic imaging.

The aim of this review is threefold: (i) introducing deep learning with some important basics, (ii) reviewing recent works that apply deep learning in the entire ecological chain of photoacoustic imaging, from image reconstruction to disease diagnosis, (iii) providing some open source materials and other resources for researchers interested in applying deep learning to photoacoustic imaging.

机器学习在过去几年中得到了飞速发展，并见证了各个领域的许多应用。这种繁荣始于2009年，当时出现了一种新的模型，即深度人工神经网络，该模型在某些重要基准上开始超越其他已建立的成熟模型。后来，它被广泛用于学术界和工业界。从图像分析到自然语言处理，它充分发挥了魔力，现在已成为最先进的机器学习模型。深度神经网络在医学成像技术，医学数据分析，医学诊断和其他医疗保健方面具有巨大潜力，并且在临床前甚至临床阶段都得到了推广。在这篇评论中

这篇综述的目的是三方面的：（i）介绍深度学习和一些重要的基础知识，（ii）回顾将深度学习应用于光声成像整个生态链中的最新工作，从图像重建到疾病诊断，（iii）提供一些内容。开源材料和其他资源，供有兴趣将深度学习应用于光声成像的研究人员使用。