A survey on active learning and human-in-the-loop deep learning for medical image analysis
A survey on active learning and human-in-the-loop deep learning for medical image analysis
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DOI：
10.1016/j.media.2021.102062
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发表时间：
2021-04-23
影响因子：
10.9
通讯作者：
Kainz, Bernhard
中科院分区：
文献类型：
--
作者：
Budd, Samuel;Robinson, Emma C.;Kainz, Bernhard
全自动深度学习已经成为许多任务的最先进技术，包括图像采集、分析和解释，以及为计算机辅助检测、诊断、治疗计划、干预和治疗提取临床有用信息。然而，医学图像分析带来的独特挑战表明，在任何支持深度学习的系统中保留人类最终用户将是有益的。在这篇综述中，我们调查了人类在支持深度学习的诊断应用程序的开发和部署中可能扮演的角色，并将重点放在将保留来自人类最终用户的重要输入的技术上。由于医疗领域工作的安全关键性质，人在环路计算是我们认为在未来研究中越来越重要的一个领域。我们评估了我们认为对临床实践中的深度学习至关重要的四个关键领域：(1)主动学习，以选择最佳数据来注释以获得最佳模型性能；(2)与模型输出的交互-使用迭代反馈将模型引导到给定预测的最优，并提供有意义的方法来解释和响应预测；(3)实用考虑--开发全面的应用程序，以及在部署之前需要做出的关键考虑事项；(4)未来的前瞻性和未回答的问题--知识缺口和相关研究领域，将在其发展过程中造福于人在环中计算。我们对最有希望的研究方向以及如何将每个领域的各个方面统一起来以实现共同目标提出我们的意见。(C)2021年爱思唯尔B.V.保留所有权利。
Fully automatic deep learning has become the state-of-the-art technique for many tasks including image acquisition, analysis and interpretation, and for the extraction of clinically useful information for computer-aided detection, diagnosis, treatment planning, intervention and therapy. However, the unique challenges posed by medical image analysis suggest that retaining a human end-user in any deep learning enabled system will be beneficial. In this review we investigate the role that humans might play in the development and deployment of deep learning enabled diagnostic applications and focus on techniques that will retain a significant input from a human end user. Human-in-the-Loop computing is an area that we see as increasingly important in future research due to the safety-critical nature of working in the medical domain. We evaluate four key areas that we consider vital for deep learning in the clinical practice: (1) Active Learning to choose the best data to annotate for optimal model performance; (2) Interaction with model outputs -using iterative feedback to steer models to optima for a given prediction and offering meaningful ways to interpret and respond to predictions; (3) Practical considerations - developing full scale applications and the key considerations that need to be made before deployment; (4) Future Prospective and Unanswered Questions -knowledge gaps and related research fields that will benefit human-in-the-loop computing as they evolve. We offer our opinions on the most promising directions of research and how various aspects of each area might be unified towards common goals.(c) 2021 Elsevier B.V. All rights reserved.