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况的金标准。由于计算机辅助诊断系统的发展能够支持医生的临床操作，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了广泛应用且发展成熟。本文报道了针对不同病变程度的ICA图像进行二分类的深度学习新性能分析。为实现这一目标，采用了包含真实标注、病变位置及七种严重程度（范围0%-100%）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。我们旨在研究不同病变程度纳入阳性类别时对二分类性能的影响。因此，采用五种已知卷积神经网络架构，通过逐步纳入不同病变程度范围的输入图像进行训练，直至覆盖全部七种病变程度。此外，设计了四类有无数据增强的实验，并计算其F-measure和曲线下面积（AUC）。报告结果显示F-measure和AUC分别达到92.7%和98.1%。然而，病变分类准确率受目标分类病变程度影响显著，当存在<99%病变图像块时准确率降低15%。