Motivation: Medical image analysis involves tasks to assist physicians in qualitative and quantitative analysis of lesions or anatomical structures, significantly improving the accuracy and reliability of diagnosis and prognosis. Traditionally, these tasks are finished by physicians or medical physicists and lead to two major problems: (i) low efficiency; (ii) biased by personal experience. In the past decade, many machine learning methods have been applied to accelerate and automate the image analysis process. Compared to the enormous deployments of supervised and unsupervised learning models, attempts to use reinforcement learning in medical image analysis are scarce. This review article could serve as the stepping-stone for related research. Significance: From our observation, though reinforcement learning has gradually gained momentum in recent years, many researchers in the medical analysis field find it hard to understand and deploy in clinics. One cause is lacking well-organized review articles targeting readers lacking professional computer science backgrounds. Rather than providing a comprehensive list of all reinforcement learning models in medical image analysis, this paper may help the readers to learn how to formulate and solve their medical image analysis research as reinforcement learning problems. Approach & Results: We selected published articles from Google Scholar and PubMed. Considering the scarcity of related articles, we also included some outstanding newest preprints. The papers are carefully reviewed and categorized according to the type of image analysis task. We first review the basic concepts and popular models of reinforcement learning. Then we explore the applications of reinforcement learning models in landmark detection. Finally, we conclude the article by discussing the reviewed reinforcement learning approaches' limitations and possible improvements.
翻译:动力:医学图像分析涉及协助医生对损伤或解剖结构进行定性和定量分析的任务,大大提高诊断和预测的准确性和可靠性。传统上,这些任务由医生或医学物理学家完成,并导致两大问题:(一) 效率低;(二) 个人经验有偏向。在过去的十年中,许多机器学习方法被用于加速和自动化图像分析过程。与大量部署受监管和不受监督的学习模式相比,试图在医学图像分析中使用强化学习的尝试很少。本评论文章可以作为相关研究的跳板。说明:从我们的观察中,虽然加强学习逐渐获得动力,但许多医学分析领域的研究人员发现难以理解和在诊所部署。一个原因是缺乏针对缺乏专业计算机科学背景的读者的精心组织的审查文章。与提供医学图像分析中所有强化学习模式的综合清单相比,本文可能有助于读者学习如何制定和解决其医学图像分析作为强化学习问题的可能。我们通过认真研究与结果:我们先期出版的强化基础研究论文,从谷歌和软体分析模型的升级和升级分析中,我们先期阅读了最后的论文。