论文标题
一种深层的合奏学习方法，用于肺CT分割，以评估COVID的严重性评估
A Deep Ensemble Learning Approach to Lung CT Segmentation for COVID-19 Severity Assessment
论文作者
论文摘要
我们提出了一种新型的深度学习方法，以分类COVID-19患者的肺CTS分割。具体而言，我们将扫描分为健康的肺组织，非肺区域，以及两个不同但视觉上相似的病理性肺组织，即地面玻璃透明度和巩固。这是通过独特的端到端层次结构架构和整体学习来完成的，这有助于分割并为细分不确定性提供衡量标准。提议的框架为三个Covid-19数据集实现了竞争成果和出色的概括能力。我们的方法在COVID-19 CT图像细分的公共Kaggle竞赛中排名第二。此外，显示分割不确定性区域与两个不同放射科医生的手动注释之间的分歧相对应。最后，在比较患者的COVID-19严重程度评分（基于临床指标）和分割的肺病理时，显示了我们的私人数据集的初步有希望的对应结果。代码和数据可在我们的存储库中找到：https：//github.com/talbenha/covid-seg
We present a novel deep learning approach to categorical segmentation of lung CTs of COVID-19 patients. Specifically, we partition the scans into healthy lung tissues, non-lung regions, and two different, yet visually similar, pathological lung tissues, namely, ground-glass opacity and consolidation. This is accomplished via a unique, end-to-end hierarchical network architecture and ensemble learning, which contribute to the segmentation and provide a measure for segmentation uncertainty. The proposed framework achieves competitive results and outstanding generalization capabilities for three COVID-19 datasets. Our method is ranked second in a public Kaggle competition for COVID-19 CT images segmentation. Moreover, segmentation uncertainty regions are shown to correspond to the disagreements between the manual annotations of two different radiologists. Finally, preliminary promising correspondence results are shown for our private dataset when comparing the patients' COVID-19 severity scores (based on clinical measures), and the segmented lung pathologies. Code and data are available at our repository: https://github.com/talbenha/covid-seg
