Abstract
In the realm of cardiovascular medicine, medical imaging plays a crucial role in accurately classifying cardiac diseases and making precise diagnoses. However, the field faces significant challenges when integrating data science techniques, as a significant volume of images is required for these techniques. As a consequence, it is necessary to investigate different avenues to overcome this challenge. In this contribution, we offer an innovative tool to conquer this limitation. In particular, we delve into the application of a well recognized method known as the EigenFaces approach to classify cardiac diseases. This approach was originally motivated for efficiently representing pictures of faces using principal component analysis, which provides a set of eigenvectors (aka eigenfaces), explaining the variation between face images. As this approach proven to be efficient for face recognition, it motivated us to explore its efficiency on more complicated data bases. In particular, we integrate this approach, with convolutional neural networks (CNNs) to classify echocardiography images taken from mice in five distinct cardiac conditions (healthy, diabetic cardiomyopathy, myocardial infarction, obesity and TAC hypertension). Performing a preprocessing step inspired from the eigenfaces approach on the echocardiography datasets, yields sets of pod modes, which we will call eigenhearts. To demonstrate the proposed approach, we compare two testcases: (i) supplying the CNN with the original images directly, (ii) supplying the CNN with images projected into the obtained pod modes. The results show a substantial and noteworthy enhancement when employing SVD for pre-processing, with classification accuracy increasing by approximately 50%.
Abstract (translated)
在心血管医学领域，医学成像在准确分类心脏疾病和进行精确诊断中起着至关重要的作用。然而，在将数据科学技术融入这一领域时面临着显著挑战，因为这些技术需要大量的图像数据。因此，有必要探索不同的途径来克服这个挑战。本文提供了一个创新工具以攻克这一限制。特别是，我们探讨了一种广泛应用的方法——EigenFaces方法在分类心脏疾病中的应用。该方法最初是为使用主成分分析（PCA）高效表示面部图片而设计的，它提供了一组特征向量（即特征脸），解释了人脸图像之间的变化。鉴于这种方法在人脸识别上的有效性，这激发了我们探索其在更复杂数据库上有效性的兴趣。特别是，我们将此方法与卷积神经网络（CNNs）相结合，用于分类来自五种不同心脏状况的小鼠的超声心动图图像（健康、糖尿病心肌病、心肌梗死、肥胖和TAC高血压）。我们在超声心动图数据集上执行了受EigenFaces方法启发的预处理步骤，产生了我们称为特征心脏的一组模态。为了展示所提出的方案，我们将两种测试案例进行了比较：(i) 直接向CNN提供原始图像；(ii) 向CNN提供投影到获得的模态中的图像。结果显示，在使用奇异值分解（SVD）进行预处理时有显著且值得注意的改进，分类准确率提高了大约50%。
URL
https://arxiv.org/abs/2411.16227
