人工智能引导放射学系统中的伪相关检测与健全性检验
Detecting Spurious Correlations with Sanity Tests for Artificial Intelligence Guided Radiology Systems
摘要：
人工智能（AI）已经成功地解决了机器感知中的许多问题。在放射学领域，人工智能系统正在迅速发展，并在指导治疗决策、诊断、在医学图像上定位疾病以及提高放射科医生的效率方面取得了进展。在放射学中部署人工智能的一个关键组成部分是对开发系统的有效性和安全性充满信心。目前的黄金标准方法是对来自一个或多个机构的泛化数据集进行性能分析验证，然后在部署期间对系统的有效性进行临床验证研究。临床验证研究非常耗时，并且最佳实践要求分析验证数据的重复使用有限，因此最好提前了解系统是否可能无法通过分析或临床验证。在本文中，我们描述了一系列的健全性测试，以确定当一个系统执行良好的开发数据错误的原因。我们通过设计一个深度学习系统来对计算机断层扫描中看到的胰腺癌进行分类，来说明健全性测试的价值。
英文摘要：
Artificial intelligence (AI) has been successful at solving numerous problems in machine perception. In radiology, AI systems are rapidly evolving and show progress in guiding treatment decisions, diagnosing, localizing disease on medical images, and improving radiologists' efficiency. A critical component to deploying AI in radiology is to gain confidence in a developed system's efficacy and safety. The current gold standard approach is to conduct an analytical validation of performance on a generalization dataset from one or more institutions, followed by a clinical validation study of the system's efficacy during deployment. Clinical validation studies are time-consuming, and best practices dictate limited re-use of analytical validation data, so it is ideal to know ahead of time if a system is likely to fail analytical or clinical validation. In this paper, we describe a series of sanity tests to identify when a system performs well on development data for the wrong reasons. We illustrate the sanity tests' value by designing a deep learning system to classify pancreatic cancer seen in computed tomography scans.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
