论文标题
在深度学习中利用胰腺腺癌分级的不确定性
Leveraging Uncertainty in Deep Learning for Pancreatic Adenocarcinoma Grading
论文作者
论文摘要
与其他癌症相比，胰腺癌具有最差的预后之一，因为它们已被诊断出癌症已朝着后期阶段发展。当前用于诊断胰腺腺癌的手动组织学分级是耗时的，并且通常会导致误诊。在数字病理学中，基于AI的癌症分级必须在预测和不确定性量化方面非常准确，以提高可靠性和解释性，对于获得临床医生对技术的信任至关重要。我们提出了MGG自动化胰腺癌分级的贝叶斯卷积神经网络，他对图像进行了染色，以估计模型预测中的不确定性。我们表明，估计的不确定性与预测误差相关。具体而言，它对于使用权衡分类准确性 - 拒绝权衡和错误分类成本的指标来设置验收阈值很有用，可以通过超参数控制，并且可以在临床环境中使用。
Pancreatic cancers have one of the worst prognoses compared to other cancers, as they are diagnosed when cancer has progressed towards its latter stages. The current manual histological grading for diagnosing pancreatic adenocarcinomas is time-consuming and often results in misdiagnosis. In digital pathology, AI-based cancer grading must be extremely accurate in prediction and uncertainty quantification to improve reliability and explainability and are essential for gaining clinicians trust in the technology. We present Bayesian Convolutional Neural Networks for automated pancreatic cancer grading from MGG and HE stained images to estimate uncertainty in model prediction. We show that the estimated uncertainty correlates with prediction error. Specifically, it is useful in setting the acceptance threshold using a metric that weighs classification accuracy-reject trade-off and misclassification cost controlled by hyperparameters and can be employed in clinical settings.
