PDF(Pubmed)
Abstract：
The uncertainty of true labels in medical images hinders diagnosis owing to the variability across professionals when applying deep learning models. We used deep learning to obtain an optimal convolutional neural network (CNN) by adequately annotating data for oral exfoliative cytology considering labels from multiple oral pathologists. Six whole-slide images were processed using QuPath for segmenting them into tiles. The images were labeled by three oral pathologists, resulting in 14,535 images with the corresponding pathologists\' annotations. Data from three pathologists who provided the same diagnosis were labeled as ground truth (GT) and used for testing. We investigated six models trained using the annotations of (1) pathologist A, (2) pathologist B, (3) pathologist C, (4) GT, (5) majority voting, and (6) a probabilistic model. We divided the test by cross-validation per slide dataset and examined the classification performance of the CNN with a ResNet50 baseline. Statistical evaluation was performed repeatedly and independently using every slide 10 times as test data. For the area under the curve, three cases showed the highest values (0.861, 0.955, and 0.991) for the probabilistic model. Regarding accuracy, two cases showed the highest values (0.988 and 0.967). For the models using the pathologists and GT annotations, many slides showed very low accuracy and large variations across tests. Hence, the classifier trained with probabilistic labels provided the optimal CNN for oral exfoliative cytology considering diagnoses from multiple pathologists. These results may lead to trusted medical artificial intelligence solutions that reflect diverse diagnoses of various professionals.
摘要：
医学图像中真实标签的不确定性阻碍了诊断，因为在应用深度学习模型时，专业人员之间存在差异。我们使用深度学习，通过考虑来自多个口腔病理学家的标签，充分注释口腔脱落细胞学的数据，获得最佳的卷积神经网络（CNN）。使用QuPath处理六个全幻灯片图像以将其分割为图块。这些图像由三名口腔病理学家标记，产生14,535张图像，并附有相应的病理学家注释。来自提供相同诊断的三名病理学家的数据被标记为地面实况（GT）并用于测试。我们调查了使用(1)病理学家A的注释训练的六个模型，(2)病理学家B,(3)病理学家C,(4)GT,（5）多数票，(6)概率模型。我们通过每个幻灯片数据集的交叉验证来划分测试，并使用ResNet50基线检查CNN的分类性能。使用每个载玻片重复和独立地进行统计评估10次作为测试数据。对于曲线下的面积，3例显示概率模型的最高值(0.861,0.955和0.991).关于准确性，2例表现为最高值(0.988和0.967)。对于使用病理学家和GT注释的模型，许多幻灯片显示出非常低的准确性和大的变化在测试。因此，考虑到多个病理学家的诊断,用概率标签训练的分类器为口腔脱落细胞学提供了最佳CNN.这些结果可能会导致值得信赖的医疗人工智能解决方案，反映各种专业人员的不同诊断。
