论文标题
基于深度学习的HPV状态预测口咽癌患者
Deep Learning Based HPV Status Prediction for Oropharyngeal Cancer Patients
论文作者
论文摘要
我们研究了深度学习模型基于成像的HPV状态检测的能力。为了克服小型医疗数据集的问题，我们使用了转移学习方法。对体育视频片段预先训练的3D卷积网络进行了微调，以便可以利用CT图像中的完整3D信息。视频预训练的模型能够将HPV阳性与HPV阴性案例区分开，而外部测试集的接收器操作特征曲线（AUC）下方的区域为0.81。与经过从头开始训练的3D卷积神经网络（CNN）和ImageNet预先训练的2D体系结构相比，视频预训练的模型表现最好。
We investigated the ability of deep learning models for imaging based HPV status detection. To overcome the problem of small medical datasets we used a transfer learning approach. A 3D convolutional network pre-trained on sports video clips was fine tuned such that full 3D information in the CT images could be exploited. The video pre-trained model was able to differentiate HPV-positive from HPV-negative cases with an area under the receiver operating characteristic curve (AUC) of 0.81 for an external test set. In comparison to a 3D convolutional neural network (CNN) trained from scratch and a 2D architecture pre-trained on ImageNet the video pre-trained model performed best.
