医学超声成像强化学习综合综述。

IF 13.9 2区 计算机科学 Q1 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE
Hanae Elmekki, Saidul Islam, Ahmed Alagha, Hani Sami, Amanda Spilkin, Ehsan Zakeri, Antonela Mariel Zanuttini, Jamal Bentahar, Lyes Kadem, Wen-Fang Xie, Philippe Pibarot, Rabeb Mizouni, Hadi Otrok, Shakti Singh, Azzam Mourad
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引用次数: 0

摘要

医学超声(US)成像在过去几年中需求不断增加,由于其可负担性、便携性和实时性,成为临床实践中最受欢迎的成像方式之一。然而,它面临着一些限制其适用性的挑战,例如操作员依赖性、解释的可变性和有限的分辨率,这些都被训练有素的专家的低可用性放大了。这就需要能够减少对人类依赖的自主系统,以提高效率和吞吐量。强化学习(RL)是人工智能(AI)下一个快速发展的领域,它允许通过与环境的奖励互动来开发自主和智能的代理。现有的几项关于美国成像进展的调查主要集中在部分自主的人工智能解决方案上。然而,这些调查都没有探讨美国流程的各个阶段与RL解决方案的最新进展之间的交集。为了弥补这一差距,本调查提出了一个综合的分类法,该分类法将美国过程的各个阶段与强化学习开发管道(包括数据准备、问题制定、模拟环境、强化学习训练、验证和微调)相结合,并在该分类法下回顾了当前的研究工作。这项工作旨在强调强化学习在构建自主美国解决方案方面的潜力,同时确定该领域进一步发展的限制和机会。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Comprehensive review of reinforcement learning for medical ultrasound imaging

Medical Ultrasound (US) imaging has seen increasing demands over the past years, becoming one of the most preferred imaging modalities in clinical practice due to its affordability, portability, and real-time capabilities. However, it faces several challenges that limit its applicability, such as operator dependency, variability in interpretation, and limited resolution, which are amplified by the low availability of trained experts. This calls for the need of autonomous systems that are capable of reducing the dependency on humans for increased efficiency and throughput. Reinforcement Learning (RL) comes as a rapidly advancing field under Artificial Intelligence (AI) that allows the development of autonomous and intelligent agents through rewarded interactions with their environments. Several existing surveys on advancements in US imaging predominantly focus on partially autonomous AI solutions. However, none of these surveys explore the intersection between the stages of the US process and the recent advancements in RL solutions. To bridge this gap, this survey proposes a comprehensive taxonomy that integrates the stages of the US process with the RL development pipeline -including data preparation, problem formulation, simulation environment, RL training, validation and finetuning- and reviews current research efforts under this taxonomy. This work aims to highlight the potential of RL in building autonomous US solutions while identifying limitations and opportunities for further advancements in this field.

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来源期刊
Artificial Intelligence Review
Artificial Intelligence Review 工程技术-计算机:人工智能
CiteScore
22.00
自引率
3.30%
发文量
194
审稿时长
5.3 months
期刊介绍: Artificial Intelligence Review, a fully open access journal, publishes cutting-edge research in artificial intelligence and cognitive science. It features critical evaluations of applications, techniques, and algorithms, providing a platform for both researchers and application developers. The journal includes refereed survey and tutorial articles, along with reviews and commentary on significant developments in the field.
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