In the application of deep learning on optical coherence tomography (OCT) data, it is common to train classification networks using 2D images originating from volumetric data. Given the micrometer resolution of OCT systems, consecutive images are often very similar in both visible structures and noise. Thus, an inappropriate data split can result in overlap between the training and testing sets, with a large portion of the literature overlooking this aspect. In this study, the effect of improper dataset splitting on model evaluation is demonstrated for three classification tasks using three OCT open-access datasets extensively used, Kermany's and Srinivasan's ophthalmology datasets, and AIIMS breast tissue dataset. Results show that the classification performance is inflated by 0.07 up to 0.43 in terms of Matthews Correlation Coefficient (accuracy: 5% to 30%) for models tested on datasets with improper splitting, highlighting the considerable effect of dataset handling on model evaluation. This study intends to raise awareness on the importance of dataset splitting given the increased research interest in implementing deep learning on OCT data.
翻译:在应用光学一致性断层学(OCT)数据方面,使用来自体积数据的2D图像对分类网络进行培训是常见的。鉴于OCT系统的微分解度,连续图像在可见结构和噪音方面往往非常相似。因此,不适当的数据分割可能导致培训和测试组之间的重叠,大部分文献都忽略了这一方面。在这项研究中,利用广泛使用的三种 OCT 开放存取数据集、 Kermany 和 Srinivasan 的眼科数据集和 AIMS 乳房组织数据集,对模式评估进行了三次分类任务,显示了不适当的数据集分割对模型评估的影响。研究结果表明,由于对实施对OCT数据进行深入研究的研究兴趣增加,因此对在数据组中测试的模型的分类性能(精确度:5%至30%)增加了0.07至0.43的升幅(精确度:5%至30%),突出数据集处理对模型评估的巨大影响。这项研究的目的是提高对数据分离重要性的认识。