标题： 基于胸部X光片的肺部疾病严重程度分类的深度主动学习：在类别不平衡情况下用更少数据进行学习 显示英文标题

标题： Deep Active Learning for Lung Disease Severity Classification from Chest X-rays: Learning with Less Data in the Presence of Class Imbalance

摘要： 为在类别不平衡情况下减少用于胸部X光片（CXRs）肺部疾病严重程度分类所需的标记数据量，本研究应用了带有贝叶斯神经网络（BNN）近似和加权损失函数的深度主动学习。这项回顾性研究收集了2020年1月至11月在埃默里医疗保健附属医院从963名患者（平均年龄，59.2$\pm$16.6岁；481名女性）中获取的2,319张CXRs。所有患者均经临床确诊感染新冠病毒。每张CXR均由3至6名经过认证的放射科医生独立标记为正常、中度或严重。使用主动学习训练了一个具有蒙特卡洛Dropout的深度神经网络来对疾病严重程度进行分类。使用各种采集函数从未标记池中迭代选择最有信息量的样本。性能通过准确率、受试者工作特征曲线下的面积（AU ROC）和精确率-召回率曲线下的面积（AU PRC）进行评估。记录了训练时间和采集时间。统计分析包括描述性指标和不同采集策略之间的性能比较。熵采样在二分类（正常与患病）中使用15.4%的训练数据达到了93.7%的准确率（AU ROC，0.91）。在多类设置中，平均标准差采样使用23.1%的标记数据达到了70.3%的准确率（AU ROC，0.86）。这些方法优于更复杂且计算成本更高的采集函数，并显著减少了标记需求。带有BNN近似和加权损失的深度主动学习有效减少了标记数据需求，同时解决了类别不平衡问题，保持或超过了诊断性能。 显示英文摘要

摘要： To reduce the amount of required labeled data for lung disease severity classification from chest X-rays (CXRs) under class imbalance, this study applied deep active learning with a Bayesian Neural Network (BNN) approximation and weighted loss function. This retrospective study collected 2,319 CXRs from 963 patients (mean age, 59.2 $\pm$ 16.6 years; 481 female) at Emory Healthcare affiliated hospitals between January and November 2020. All patients had clinically confirmed COVID-19. Each CXR was independently labeled by 3 to 6 board-certified radiologists as normal, moderate, or severe. A deep neural network with Monte Carlo Dropout was trained using active learning to classify disease severity. Various acquisition functions were used to iteratively select the most informative samples from an unlabeled pool. Performance was evaluated using accuracy, area under the receiver operating characteristic curve (AU ROC), and area under the precision-recall curve (AU PRC). Training time and acquisition time were recorded. Statistical analysis included descriptive metrics and performance comparisons across acquisition strategies. Entropy Sampling achieved 93.7% accuracy (AU ROC, 0.91) in binary classification (normal vs. diseased) using 15.4% of the training data. In the multi-class setting, Mean STD sampling achieved 70.3% accuracy (AU ROC, 0.86) using 23.1% of the labeled data. These methods outperformed more complex and computationally expensive acquisition functions and significantly reduced labeling needs. Deep active learning with BNN approximation and weighted loss effectively reduces labeled data requirements while addressing class imbalance, maintaining or exceeding diagnostic performance.