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Deep learning for photoacoustic imaging a survey

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Document pages: 15 pages

Abstract: Machine learning has been developed dramatically and witnessed a lot ofapplications in various fields over the past few years. This boom originated in2009, when a new model emerged, that is, the deep artificial neural network,which began to surpass other established mature models on some importantbenchmarks. Later, it was widely used in academia and industry. Ranging fromimage analysis to natural language processing, it fully exerted its magic andnow become the state-of-the-art machine learning models. Deep neural networkshave great potential in medical imaging technology, medical data analysis,medical diagnosis and other healthcare issues, and is promoted in bothpre-clinical and even clinical stages. In this review, we performed an overviewof some new developments and challenges in the application of machine learningto medical image analysis, with a special focus on deep learning inphotoacoustic imaging. The aim of this review is threefold: (i) introducingdeep learning with some important basics, (ii) reviewing recent works thatapply deep learning in the entire ecological chain of photoacoustic imaging,from image reconstruction to disease diagnosis, (iii) providing some opensource materials and other resources for researchers interested in applyingdeep learning to photoacoustic imaging.

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