Heart Failure is a major component of healthcare expenditure and a leading cause of mortality worldwide. Despite higher inter-rater variability, endomyocardial biopsy is still regarded as the standard technique, used to identify the cause (e.g. ischemic or non-ischemic cardiomyopathy, coronary artery disease, myocardial infarction etc) of unexplained heart failure. In this paper, we focus on identifying cardiomyopathy as ischemic or non-ischemic. For this, we propose and implement a new unified architecture comprising convolutional neural network (inception-V3 model) and bidirectional long short-term memory (BiLSTM) with self-attention mechanism to predict the ischemic or non-ischemic to classify cardiomyopathy using histopathological images. The proposed model is based on self-attention that implicitly focuses on the information outputted from the hidden layers of BiLSTM. Through our results, we demonstrate that this framework carries a high learning capacity and is able to imp...

中文翻译：

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基于自注意的双向长时短时记忆-卷积神经网络分类器，用于预测缺血性和非缺血性心肌病

心力衰竭是医疗保健支出的主要组成部分，也是全球范围内死亡的主要原因。尽管评分者之间存在较高的变异性，但心内膜活检仍被视为标准技术，用于确定无法解释的心力衰竭的原因（例如缺血性或非缺血性心肌病，冠心病，心肌梗塞等）。在本文中，我们着重于将心肌病识别为缺血性或非缺血性。为此，我们提出并实现了一个新的统一体系结构，该体系结构包括卷积神经网络（inception-V3模型）和双向长短期记忆（BiLSTM），具有自我注意机制，可以使用组织病理学图像预测缺血性或非缺血性心肌病的分类。所提出的模型基于自我关注，后者将注意力集中在BiLSTM隐藏层输出的信息上。通过我们的结果，我们证明了该框架具有很高的学习能力，并且能够促进...