In clinical settings, a lot of medical image datasets suffer from the imbalance problem which hampers the detection of outliers (rare health care events), as most classification methods assume an equal occurrence of classes. In this way, identifying outliers in imbalanced datasets has become a crucial issue. To help address this challenge, one-class classification, which focuses on learning a model using samples from only a single given class, has attracted increasing attention. Previous one-class modeling usually uses feature mapping or feature fitting to enforce the feature learning process. However, these methods are limited for medical images which usually have complex features. In this paper, a novel method is proposed to enable deep learning models to optimally learn single-class-relevant inherent imaging features by leveraging the concept of imaging complexity. We investigate and compare the effects of simple but effective perturbing operations applied to images to capture imaging complexity and to enhance feature learning. Extensive experiments are performed on four clinical datasets to show that the proposed method outperforms four state-of-the-art methods.

在临床环境中，许多医学图像数据集都存在不平衡问题，这阻碍了异常值（罕见的医疗保健事件）的检测，因为大多数分类方法都假设类的发生率相同。通过这种方式，识别不平衡数据集中的异常值已成为一个关键问题。为了帮助应对这一挑战，专注于仅使用来自单个给定类的样本来学习模型的一类分类引起了越来越多的关注。以前的一类建模通常使用特征映射或特征拟合来强制执行特征学习过程。然而，这些方法仅限于通常具有复杂特征的医学图像。在这篇论文中，提出了一种新方法，通过利用成像复杂性的概念，使深度学习模型能够最佳地学习与单类相关的固有成像特征。我们调查并比较了简单但有效的扰动操作对图像的影响，以捕捉成像复杂性并增强特征学习。在四个临床数据集上进行了大量实验，以表明所提出的方法优于四种最先进的方法。