论文标题
不确定性估计技术对肝病变检测假阳性降低的影响
Influence of uncertainty estimation techniques on false-positive reduction in liver lesion detection
论文作者
论文摘要
深度学习技术在检测医学图像中的对象方面取得了成功，但仍然遭受虚假阳性预测，可能会阻碍准确的诊断。神经网络输出的估计不确定性已用于标记不正确的预测。我们研究了从神经网络不确定性估计和基于形状的特征计算出的功能，这些特征是通过为不同的不确定性估计方法开发基于分类的后处理步骤来降低肝病变检测中的假阳性。对于两个数据集上的所有不确定性估计方法，我们证明了神经网络的病变检测性能的改善，分别包括腹部MR和CT图像。我们表明，根据神经网络不确定性估计计算的功能往往不会有助于降低假阳性。我们的结果表明，从不确定性图中提取的类别不平衡（真实的假阳性比率）和基于形状的特征等因素在区分假阳性和真实阳性预测方面起着重要作用。我们的代码可以在https://github.com/ishaanb92/fpcpipeline上找到。
Deep learning techniques show success in detecting objects in medical images, but still suffer from false-positive predictions that may hinder accurate diagnosis. The estimated uncertainty of the neural network output has been used to flag incorrect predictions. We study the role played by features computed from neural network uncertainty estimates and shape-based features computed from binary predictions in reducing false positives in liver lesion detection by developing a classification-based post-processing step for different uncertainty estimation methods. We demonstrate an improvement in the lesion detection performance of the neural network (with respect to F1-score) for all uncertainty estimation methods on two datasets, comprising abdominal MR and CT images, respectively. We show that features computed from neural network uncertainty estimates tend not to contribute much toward reducing false positives. Our results show that factors like class imbalance (true over false positive ratio) and shape-based features extracted from uncertainty maps play an important role in distinguishing false positive from true positive predictions. Our code can be found at https://github.com/ishaanb92/FPCPipeline.
