用强化学习优化超声图像分析的决策流程，提升诊断效率与准确性。
Reinforcement Learning for Ultrasound Image Analysis A Comprehensive Review of Advances and Applications
- 结合深度学习与强化学习，实现超声图像处理的智能序列决策。
- 梳理14篇相关研究，覆盖分类、分割、增强等五大应用方向。
- 适合关注医疗AI、序列决策模型的研究者与临床工程师。
过去十年中，机器学习在医学领域的应用显著增长，多数基于深度学习，旨在从网格数据（如医学图像）中学习表征。然而，强化学习（RL）在医学中的应用仍相对有限。医学诊断常涉及一系列子任务构成的流程，而强化学习特别适合优化此类序列决策问题。超声图像分析正是典型的序列决策任务：超声探头采集原始信号后，需经过一系列信号处理和图像后处理步骤，最终生成诊断建议。尽管如此，强化学习在超声中的应用仍较局限。深度强化学习（DRL）融合深度学习与强化学习，有望通过智能序列决策优化整个分析流程。本文综述了近十年来强化学习在超声图像分析中的进展。我们简要介绍强化学习的理论框架及其在超声图像处理中的应用，并系统回顾了图像分析全流程中现有研究。通过在Scopus上进行综合检索并筛选出14篇最相关论文，按目标应用分为图像分类、分割、增强、视频摘要及自动导航与路径规划。同时分析各研究采用的强化学习方法类型。最后，讨论了深度强化学习在超声中可用于序列决策的关键医疗领域，剖析其机遇、挑战与局限，为未来发展方向提供洞见。
原文摘要 · Abstract (English)
Over the last decade, the use of machine learning (ML) approaches in medicinal applications has increased manifold. Most of these approaches are based on deep learning, which aims to learn representations from grid data (like medical images). However, reinforcement learning (RL) applications in medicine are relatively less explored. Medical applications often involve a sequence of subtasks that form a diagnostic pipeline, and RL is uniquely suited to optimize over such sequential decision-making tasks. Ultrasound (US) image analysis is a quintessential example of such a sequential decision-making task, where the raw signal captured by the US transducer undergoes a series of signal processing and image post-processing steps, generally leading to a diagnostic suggestion. The application of RL in US remains limited. Deep Reinforcement Learning (DRL), that combines deep learning and RL, holds great promise in optimizing these pipelines by enabling intelligent and sequential decision-making. This review paper surveys the applications of RL in US over the last decade. We provide a succinct overview of the theoretic framework of RL and its application in US image processing and review existing work in each aspect of the image analysis pipeline. A comprehensive search of Scopus filtered on relevance yielded 14 papers most relevant to this topic. These papers were further categorized based on their target applications image classification, image segmentation, image enhancement, video summarization, and auto navigation and path planning. We also examined the type of RL approach used in each publication. Finally, we discuss key areas in healthcare where DRL approaches in US could be used for sequential decision-making. We analyze the opportunities, challenges, and limitations, providing insights into the future potential of DRL in US image analysis.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。