Invasive Coronary Angiography (ICA) images are considered the gold standard for assessing the state of the coronary arteries. Deep learning classification methods are widely used and well-developed in different areas where medical imaging evaluation has an essential impact due to the development of computer-aided diagnosis systems that can support physicians in their clinical procedures. In this paper, a new performance analysis of deep learning methods for binary ICA classification with different lesion degrees is reported. To reach this goal, an annotated dataset of ICA images that contains the ground truth, the location of lesions and seven possible severity degrees ranging between 0% and 100% was employed. The ICA images were divided into 'lesion' or 'non-lesion' patches. We aim to study how binary classification performance is affected by the different lesion degrees considered in the positive class. Therefore, five known convolutional neural network architectures were trained with different input images where different lesion degree ranges were gradually incorporated until considering the seven lesion degrees. Besides, four types of experiments with and without data augmentation were designed, whose F-measure and Area Under Curve (AUC) were computed. Reported results achieved an F-measure and AUC of 92.7% and 98.1%, respectively. However, lesion classification is highly affected by the degree of the lesion intended to classify, with 15% less accuracy when <99% lesion patches are present.

翻译：有创冠状动脉造影（ICA）图像被视为评估冠状动脉状态的金标准。随着计算机辅助诊断系统的发展，深度学习分类方法在医学影像评估具有重要影响的各个领域得到广泛应用且发展成熟，此类系统可支持临床医师的诊断流程。本文报告了针对不同病变程度下ICA图像二元分类的深度学习新性能分析。为实现研究目标，采用包含真实标注、病灶位置及七种介于0%至100%之间的严重程度分级的ICA图像注释数据集。将ICA图像划分为"病变"或"非病变"图像块。本研究旨在探究阳性类别中纳入的不同病变程度对二元分类性能的影响。因此，使用五种已知卷积神经网络架构，对渐次纳入不同病变程度范围直至涵盖全部七种病变等级的输入图像进行训练。此外，设计四类有无数据增强的实验方案，并计算其F-measure值与曲线下面积（AUC）。实验结果显示F-measure与AUC分别达到92.7%和98.1%。然而，病变分类效果受目标分类病变程度的显著影响，当存在99%以下病变图像块时，分类准确率降低15%。