Ma Shizhou, Zhang Yifeng, Li Delong, Sun Yixin, Qiu Zhaowen, Wei Lei, Dong Suyu
College of Aulin, Northeast Forestry University, Harbin, China.
College of Computer and Control Engineering, Northeast Forestry University, Harbin, China.
Front Artif Intell. 2025 Jan 8;7:1467218. doi: 10.3389/frai.2024.1467218. eCollection 2024.
In clinical, the echocardiogram is the most widely used for diagnosing heart diseases. Different heart diseases are diagnosed based on different views of the echocardiogram images, so efficient echocardiogram view classification can help cardiologists diagnose heart disease rapidly. Echocardiogram view classification is mainly divided into supervised and semi-supervised methods. The supervised echocardiogram view classification methods have worse generalization performance due to the difficulty of labeling echocardiographic images, while the semi-supervised echocardiogram view classification can achieve acceptable results via a little labeled data. However, the current semi-supervised echocardiogram view classification faces challenges of declining accuracy due to out-of-distribution data and is constrained by complex model structures in clinical application.
To deal with the above challenges, we proposed a novel open-set semi-supervised method for echocardiogram view classification, SPEMix, which can improve performance and generalization by leveraging out-of-distribution unlabeled data. Our SPEMix consists of two core blocks, DAMix Block and SP Block. DAMix Block can generate a mixed mask that focuses on the valuable regions of echocardiograms at the pixel level to generate high-quality augmented echocardiograms for unlabeled data, improving classification accuracy. SP Block can generate a superclass pseudo-label of unlabeled data from the perspective of the superclass probability distribution, improving the classification generalization by leveraging the superclass pseudolabel.
We also evaluate the generalization of our method on the Unity dataset and the CAMUS dataset. The lightweight model trained with SPEMix can achieve the best classification performance on the publicly available TMED2 dataset.
For the first time, we applied the lightweight model to the echocardiogram view classification, which can solve the limits of the clinical application due to the complex model architecture and help cardiologists diagnose heart diseases more efficiently.
在临床上，超声心动图是诊断心脏病最广泛使用的方法。不同的心脏病是根据超声心动图图像的不同视图来诊断的，因此高效的超声心动图视图分类可以帮助心脏病专家快速诊断心脏病。超声心动图视图分类主要分为监督式和半监督式方法。由于超声心动图图像标注困难，监督式超声心动图视图分类方法的泛化性能较差，而半监督式超声心动图视图分类通过少量标注数据就能取得可接受的结果。然而，当前的半监督式超声心动图视图分类面临因分布外数据导致准确率下降的挑战，并且在临床应用中受到复杂模型结构的限制。
为应对上述挑战，我们提出了一种用于超声心动图视图分类的新型开放集半监督方法SPEMix，它可以通过利用分布外未标注数据来提高性能和泛化能力。我们的SPEMix由两个核心模块组成，即DAMix模块和SP模块。DAMix模块可以生成一个混合掩码，在像素级别聚焦于超声心动图的有价值区域，为未标注数据生成高质量的增强超声心动图，提高分类准确率。SP模块可以从超类概率分布的角度生成未标注数据的超类伪标签，通过利用超类伪标签提高分类泛化能力。
我们还在Unity数据集和CAMUS数据集上评估了我们方法的泛化能力。使用SPEMix训练的轻量级模型在公开可用的TMED2数据集上可以实现最佳分类性能。
我们首次将轻量级模型应用于超声心动图视图分类，它可以解决因复杂模型架构导致的临床应用限制，并帮助心脏病专家更高效地诊断心脏病。
