论文标题
用于肺超声检查的逆向转移和诊断学习的致密像素标记，用于肺炎19和肺炎检测
Dense Pixel-Labeling for Reverse-Transfer and Diagnostic Learning on Lung Ultrasound for COVID-19 and Pneumonia Detection
论文作者
论文摘要
我们建议使用预先训练的分割模型执行诊断分类，以实现更好的概括和解释性，从而将技术反向转移学习。我们提出了一个架构，将细分模型转换为分类模型。我们比较和对比密集与稀疏分段标记，并研究其对诊断分类的影响。我们比较了经过密集和稀疏标签的U-NET的性能与在4例肺超声扫描的自定义数据集中分段A线，B线和胸膜线的性能。我们的实验表明，密集标签有助于减少假阳性检测。我们研究了密集和稀疏训练的U-NET的分类能力，并将其与未经原始的U-NET进行对比，以检测和区分大约40K曲线和线性探测图像的大型超声数据集上的Covid-19和肺炎。我们的基于分割的模型在使用鉴定的分割权重时执行更好的分类，密集标记的U-NET表现最好。
We propose using a pre-trained segmentation model to perform diagnostic classification in order to achieve better generalization and interpretability, terming the technique reverse-transfer learning. We present an architecture to convert segmentation models to classification models. We compare and contrast dense vs sparse segmentation labeling and study its impact on diagnostic classification. We compare the performance of U-Net trained with dense and sparse labels to segment A-lines, B-lines, and Pleural lines on a custom dataset of lung ultrasound scans from 4 patients. Our experiments show that dense labels help reduce false positive detection. We study the classification capability of the dense and sparse trained U-Net and contrast it with a non-pretrained U-Net, to detect and differentiate COVID-19 and Pneumonia on a large ultrasound dataset of about 40k curvilinear and linear probe images. Our segmentation-based models perform better classification when using pretrained segmentation weights, with the dense-label pretrained U-Net performing the best.
