Researchers have shown significant correlations among segmented objects in various medical imaging modalities and disease related pathologies. Several studies showed that using hand crafted features for disease prediction neglects the immense possibility to use latent features from deep learning (DL) models which may reduce the overall accuracy of differential diagnosis. However, directly using classification or segmentation models on medical to learn latent features opt out robust feature selection and may lead to overfitting. To fill this gap, we propose a novel feature selection technique using the latent space of a segmentation model that can aid diagnosis. We evaluated our method in differentiating a rare cardiac disease: Takotsubo Syndrome (TTS) from the ST elevation myocardial infarction (STEMI) using echocardiogram videos (echo). TTS can mimic clinical features of STEMI in echo and extremely hard to distinguish. Our approach shows promising results in differential diagnosis of TTS with 82% diagnosis accuracy beating the previous state-of-the-art (SOTA) approach. Moreover, the robust feature selection technique using LASSO algorithm shows great potential in reducing the redundant features and creates a robust pipeline for short- and long-term disease prognoses in the downstream analysis.

翻译：研究人员已证明，在多种医学影像模态中，分割目标与疾病相关病理之间存在显著相关性。多项研究表明，采用手工设计特征进行疾病预测会忽略利用深度学习模型潜在特征的巨大可能性，这可能导致鉴别诊断的整体准确性降低。然而，直接使用医学图像上的分类或分割模型学习潜在特征会忽视鲁棒特征选择，并可能导致过拟合。为填补这一空白，我们提出了一种利用分割模型潜在空间的新颖特征选择技术，以辅助诊断。我们通过超声心动图视频评估了该方法在区分罕见心脏疾病——Takotsubo综合征与ST段抬高型心肌梗死中的表现。Takotsubo综合征在超声心动图中可模拟ST段抬高型心肌梗死的临床特征，极难区分。我们的方法在Takotsubo综合征的鉴别诊断中取得了令人鼓舞的结果，诊断准确率达82%，超越了此前最先进的方法。此外，基于LASSO算法的鲁棒特征选择技术在减少冗余特征方面展现出巨大潜力，并为下游分析中的短期及长期疾病预后构建了稳健的流程。