This paper considers making active learning more sensible from a medical perspective. In practice, a disease manifests itself in different forms across patient cohorts. Existing frameworks have primarily used mathematical constructs to engineer uncertainty or diversity-based methods for selecting the most informative samples. However, such algorithms do not present themselves naturally as usable by the medical community and healthcare providers. Thus, their deployment in clinical settings is very limited, if any. For this purpose, we propose a framework that incorporates clinical insights into the sample selection process of active learning that can be incorporated with existing algorithms. Our medically interpretable active learning framework captures diverse disease manifestations from patients to improve generalization performance of OCT classification. After comprehensive experiments, we report that incorporating patient insights within the active learning framework yields performance that matches or surpasses five commonly used paradigms on two architectures with a dataset having imbalanced patient distributions. Also, the framework integrates within existing medical practices and thus can be used by healthcare providers.
翻译:本文认为,从医学角度积极学习更明智。在实践中,疾病表现为不同形式的病人群。现有框架主要使用数学模型来设计不确定性或基于多样性的方法来选择信息最丰富的样本。然而,这种算法并不自然地显示自己为医疗界和保健提供者所利用。因此,在临床环境中的部署非常有限,如果有的话,也是非常有限的。为此,我们提议了一个框架,将临床见解纳入可以纳入现有算法的积极学习抽样选择过程。我们医学上可解释的积极学习框架收集了病人的各种疾病表现,以提高OCT分类的普遍性能。经过全面试验,我们报告,将病人的洞察纳入积极学习框架,在积极学习框架内产生与病人分布不平衡的两种结构相匹配或超过五种常用的功能。此外,该框架将现有医疗实践结合,因此可以供保健提供者使用。