论文标题
CHS-NET：一种深度学习方法，用于19.COVID-19感染的CT图像的分层分割
CHS-Net: A Deep learning approach for hierarchical segmentation of COVID-19 infected CT images
论文作者
论文摘要
新型SARS-COV-2也被称为Covid-19的大流行一直在全球范围内传播，导致生命猖ramp。 CT，X射线等医学成像通过呈现器官功能的视觉表示在诊断患者中起着重要作用。但是，对于任何分析此类扫描的放射科医生来说，都是一项繁琐且耗时的任务。新兴的深度学习技术在分析此类扫描方面表现出了强度，以帮助更快地诊断疾病和病毒（例如Covid-19）。在本文中，提出了一个基于深度学习的模型，即COVID-19层次分割网络（CHS-NET），提出了作为语义分层分段的功能，以通过CT使用CT医学成像从CT Medical Image进行肺轮廓的COVID-19受感染区域，并使用两个级联的残留残留注意力uneption U-Net（RAIU-NET）模型。 RAIU-NET由具有光谱空间和深度注意网络（SSD）的残留成立U-NET模型组成，这些模型是随着深度可分离卷积的收缩和膨胀阶段而开发的，以及有效地编码和解码了语义和分解语义和变态分辨率信息。 CHS-NET经过分割损失函数训练，该函数定义为二进制跨熵损失和骰子损失的平均值，以惩罚假阴性和假阳性预测。将该方法与最近提出的方法进行比较，并使用标准指标（例如精度，精度，特异性，召回，骰子系数和JACCARD相似性）进行了评估，以及使用GradCam ++和不确定性图的模型预测的可视化解释。通过广泛的试验，可以观察到所提出的方法的表现优于最近提出的方法，并有效地将肺部的共同感染区域段。
The pandemic of novel SARS-CoV-2 also known as COVID-19 has been spreading worldwide, causing rampant loss of lives. Medical imaging such as CT, X-ray, etc., plays a significant role in diagnosing the patients by presenting the visual representation of the functioning of the organs. However, for any radiologist analyzing such scans is a tedious and time-consuming task. The emerging deep learning technologies have displayed its strength in analyzing such scans to aid in the faster diagnosis of the diseases and viruses such as COVID-19. In the present article, an automated deep learning based model, COVID-19 hierarchical segmentation network (CHS-Net) is proposed that functions as a semantic hierarchical segmenter to identify the COVID-19 infected regions from lungs contour via CT medical imaging using two cascaded residual attention inception U-Net (RAIU-Net) models. RAIU-Net comprises of a residual inception U-Net model with spectral spatial and depth attention network (SSD) that is developed with the contraction and expansion phases of depthwise separable convolutions and hybrid pooling (max and spectral pooling) to efficiently encode and decode the semantic and varying resolution information. The CHS-Net is trained with the segmentation loss function that is the defined as the average of binary cross entropy loss and dice loss to penalize false negative and false positive predictions. The approach is compared with the recently proposed approaches and evaluated using the standard metrics like accuracy, precision, specificity, recall, dice coefficient and Jaccard similarity along with the visualized interpretation of the model prediction with GradCam++ and uncertainty maps. With extensive trials, it is observed that the proposed approach outperformed the recently proposed approaches and effectively segments the COVID-19 infected regions in the lungs.
