论文标题
校准图像语义细分的深度学习：关于Covid-19胸部X射线图像的案例研究
Calibrated Bagging Deep Learning for Image Semantic Segmentation: A Case Study on COVID-19 Chest X-ray Image
论文作者
论文摘要
严重的急性呼吸综合征冠状病毒2（SARS-COV-2）导致冠状病毒病2019（Covid-19）。诸如胸部X射线（CXR）和计算机断层扫描（CT）之类的成像测试可以为临床人员提供有用的信息，以更有效，更全面的方式促进Covid-19的诊断。作为人工智能（AI）的突破，已通过分析CXR和CT数据来应用深度学习来执行COVID-19感染区域分割和疾病分类。但是，对这些任务的深度学习模型的预测不确定性尚未经过全面研究，这对于关键的至关重要的应用程序非常重要。在这项工作中，我们提出了一个新颖的集合深度学习模型，通过整合包装深度学习和模型校准，不仅可以提高分割性能，还可以减少预测不确定性。所提出的方法已在与CXR图像分割相关的大数据集上进行了验证。实验结果表明，所提出的方法可以改善分割性能，并减少预测不确定性。
Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) causes coronavirus disease 2019 (COVID-19). Imaging tests such as chest X-ray (CXR) and computed tomography (CT) can provide useful information to clinical staff for facilitating a diagnosis of COVID-19 in a more efficient and comprehensive manner. As a breakthrough of artificial intelligence (AI), deep learning has been applied to perform COVID-19 infection region segmentation and disease classification by analyzing CXR and CT data. However, prediction uncertainty of deep learning models for these tasks, which is very important to safety-critical applications like medical image processing, has not been comprehensively investigated. In this work, we propose a novel ensemble deep learning model through integrating bagging deep learning and model calibration to not only enhance segmentation performance, but also reduce prediction uncertainty. The proposed method has been validated on a large dataset that is associated with CXR image segmentation. Experimental results demonstrate that the proposed method can improve the segmentation performance, as well as decrease prediction uncertainties.
