论文标题
Respirenet：一个深层神经网络，用于在有限的数据设置中准确检测异常肺部声音
RespireNet: A Deep Neural Network for Accurately Detecting Abnormal Lung Sounds in Limited Data Setting
论文作者
论文摘要
呼吸道声学的诊断是筛查和诊断肺部疾病的主要工具。自动分析，再加上数字听诊器，可以在宣传致命肺部疾病的电视筛查中发挥至关重要的作用。深度神经网络（DNN）对此类问题表现出了很多希望，并且是一个明显的选择。但是，DNN是非常饥饿的数据，最大的呼吸数据集iCbhi只有6898个呼吸周期，这对于训练令人满意的DNN模型仍然很小。在这项工作中，我们提出了一个简单的基于CNN的模型，以及一套新颖的技术 - 设备特定的微调，基于串联的增强，空白区域剪裁和智能填充 - 使我们能够有效地使用小型数据集。我们对洲际论术语数据集进行了广泛的评估，并将4级分类的最先进结果提高了2.2％
Auscultation of respiratory sounds is the primary tool for screening and diagnosing lung diseases. Automated analysis, coupled with digital stethoscopes, can play a crucial role in enabling tele-screening of fatal lung diseases. Deep neural networks (DNNs) have shown a lot of promise for such problems, and are an obvious choice. However, DNNs are extremely data hungry, and the largest respiratory dataset ICBHI has only 6898 breathing cycles, which is still small for training a satisfactory DNN model. In this work, RespireNet, we propose a simple CNN-based model, along with a suite of novel techniques -- device specific fine-tuning, concatenation-based augmentation, blank region clipping, and smart padding -- enabling us to efficiently use the small-sized dataset. We perform extensive evaluation on the ICBHI dataset, and improve upon the state-of-the-art results for 4-class classification by 2.2%
