This paper presents a novel approach to active learning that takes into account the non-independent and identically distributed (non-i.i.d.) structure of a clinical trial setting. There exists two types of clinical trials: retrospective and prospective. Retrospective clinical trials analyze data after treatment has been performed; prospective clinical trials collect data as treatment is ongoing. Typically, active learning approaches assume the dataset is i.i.d. when selecting training samples; however, in the case of clinical trials, treatment results in a dependency between the data collected at the current and past visits. Thus, we propose prospective active learning to overcome the limitations present in traditional active learning methods and apply it to disease detection in optical coherence tomography (OCT) images, where we condition on the time an image was collected to enforce the i.i.d. assumption. We compare our proposed method to the traditional active learning paradigm, which we refer to as retrospective in nature. We demonstrate that prospective active learning outperforms retrospective active learning in two different types of test settings.

翻译：本文提出了一种新颖的主动学习方法，该方法考虑了临床试验场景中非独立同分布（non-i.i.d.）的结构特性。临床试验分为两种类型：回顾性试验和前瞻性试验。回顾性临床试验在治疗完成后分析数据；前瞻性临床试验则在治疗过程中收集数据。传统的主动学习方法在选择训练样本时通常假设数据集为独立同分布（i.i.d.）；然而在临床试验中，治疗会导致当前访视与既往访视收集的数据之间存在依赖性。为此，我们提出前瞻性主动学习以克服传统主动学习方法的局限性，并将其应用于光学相干断层扫描（OCT）图像中的疾病检测任务——通过以图像采集时间为条件来强化i.i.d.假设。我们将所提方法与称为"回顾性"的传统主动学习范式进行对比，并在两种不同类型的测试场景中证明，前瞻性主动学习的性能均优于回顾性主动学习。