论文标题
对医学图像分析中学习算法的因果关系的评论
A Review of Causality for Learning Algorithms in Medical Image Analysis
论文作者
论文摘要
医学图像分析是一个充满活力的研究领域，为医生和医生提供了宝贵的见解以及准确诊断和监测疾病的能力。机器学习为该领域提供了额外的提升。但是，用于医学图像分析的机器学习尤其容易受到自然偏见的影响，例如影响算法性能和鲁棒性的领域变化。在本文中，我们在技术准备水平的框架内分析机器学习，以进行医学图像分析，并回顾因果分析方法如何在创建健壮且适应性的医学图像分析算法时如何填补空白。我们在医学成像AI/ML中使用因果关系回顾了方法，发现因果分析有可能减轻临床翻译的关键问题，但是到目前为止，吸收和临床下游研究受到限制。
Medical image analysis is a vibrant research area that offers doctors and medical practitioners invaluable insight and the ability to accurately diagnose and monitor disease. Machine learning provides an additional boost for this area. However, machine learning for medical image analysis is particularly vulnerable to natural biases like domain shifts that affect algorithmic performance and robustness. In this paper we analyze machine learning for medical image analysis within the framework of Technology Readiness Levels and review how causal analysis methods can fill a gap when creating robust and adaptable medical image analysis algorithms. We review methods using causality in medical imaging AI/ML and find that causal analysis has the potential to mitigate critical problems for clinical translation but that uptake and clinical downstream research has been limited so far.
