[论文解读] Fast and accurate classification of echocardiograms using deep learning

Ali Madani, Ramy Arnaout+2 more|arXiv (Cornell University)|Jun 27, 2017

Cardiac Imaging and DiagnosticsMedicine参考文献 9被引用 16

一句话总结

本文提出一种基于深层卷积神经网络的深度学习方法，用于从834,267张匿名图像中对15种标准经胸超声心动图视图进行分类。该模型在单张低分辨率图像上达到91.7%的准确率，在视频片段上达到97.8%的准确率，优于经过认证的超声心动图医师，展示了在自动化视图识别中具有高准确率和临床相关性。

ABSTRACT

Echocardiography is essential to modern cardiology. However, human interpretation limits high throughput analysis, limiting echocardiography from reaching its full clinical and research potential for precision medicine. Deep learning is a cutting-edge machine-learning technique that has been useful in analyzing medical images but has not yet been widely applied to echocardiography, partly due to the complexity of echocardiograms' multi view, multi modality format. The essential first step toward comprehensive computer assisted echocardiographic interpretation is determining whether computers can learn to recognize standard views. To this end, we anonymized 834,267 transthoracic echocardiogram (TTE) images from 267 patients (20 to 96 years, 51 percent female, 26 percent obese) seen between 2000 and 2017 and labeled them according to standard views. Images covered a range of real world clinical variation. We built a multilayer convolutional neural network and used supervised learning to simultaneously classify 15 standard views. Eighty percent of data used was randomly chosen for training and 20 percent reserved for validation and testing on never seen echocardiograms. Using multiple images from each clip, the model classified among 12 video views with 97.8 percent overall test accuracy without overfitting. Even on single low resolution images, test accuracy among 15 views was 91.7 percent versus 70.2 to 83.5 percent for board-certified echocardiographers. Confusional matrices, occlusion experiments, and saliency mapping showed that the model finds recognizable similarities among related views and classifies using clinically relevant image features. In conclusion, deep neural networks can classify essential echocardiographic views simultaneously and with high accuracy. Our results provide a foundation for more complex deep learning assisted echocardiographic interpretation.

研究动机与目标

• 开发一种深度学习系统，能够在真实临床图像中准确识别标准经胸超声心动图视图。
• 克服因人工解读瓶颈导致的高通量超声心动图检查的局限性。
• 在涵盖年龄、性别和体型差异的多样化真实世界超声心动图数据上验证模型性能。
• 证明深度学习能够检测与临床相关的图像特征以实现视图分类。
• 为精准医学中自动化、可扩展的超声心动图解读建立基础。

提出的方法

• 使用834,267张来自267名患者的匿名经胸超声心动图（TTE）图像，通过监督学习训练多层卷积神经网络。
• 图像根据标准经胸超声心动图视图进行标注，涵盖年龄、性别和肥胖状态下的临床差异。
• 80%的数据用于训练，20%保留用于未见研究的验证和测试。
• 模型通过处理每段视频中的多张图像，实现对12个视频视图的高准确率分类。
• 采用遮挡实验和显著性图分析来解释模型决策，验证其对临床相关特征的依赖性。
• 在单张低分辨率图像和多帧视频片段上对模型进行评估，以评估其鲁棒性。

实验结果

研究问题

• RQ1 深度学习模型能否从真实临床图像中准确分类标准经胸超声心动图视图？
• RQ2 在单张图像分类任务中，模型性能与经过认证的超声心动图医师相比如何？
• RQ3 模型在分类经胸超声心动图视图时，对临床相关图像特征的依赖程度如何？
• RQ4 单一深度学习模型能否同时以高准确率对多个标准经胸超声心动图视图进行分类？
• RQ5 该模型在未见患者人群和图像质量差异下是否具有泛化能力，且不会出现过拟合？

主要发现

• 通过每段视频中多张图像处理，模型在分类12个视频视图时整体测试准确率达到97.8%。
• 在单张低分辨率图像上，模型在15种标准视图中的分类准确率达到91.7%，超过人类专家（70.2%–83.5%）。
• 混淆矩阵显示，模型能正确将相关视图归为同一类别，表明其具备逻辑分类模式。
• 遮挡实验和显著性图证实，模型聚焦于解剖学相关区域，如心脏结构和视图特征。
• 尽管在大规模多样化临床数据上进行训练，模型未表现出过拟合迹象。
• 结果表明，深度学习可可靠识别标准经胸超声心动图视图，为临床和研究环境中的自动化提供支持。