论文标题
肺结核恶性分类使用其时间进化与两际卷积神经网络
Pulmonary Nodule Malignancy Classification Using its Temporal Evolution with Two-Stream 3D Convolutional Neural Networks
论文作者
论文摘要
结节恶性肿瘤评估是一项复杂，耗时且容易出错的任务。当前的临床实践需要测量不同时间点下结节的大小和密度的变化。最先进的解决方案依靠3D卷积神经网络建立的基于从每位患者单个CT扫描获得的肺结核上。在这项工作中，我们提出了一个两流3D卷积神经网络，该网络通过共同分析从不同时间点服用的同一患者的两个肺结核来预测恶性肿瘤。相对于从单个时间点训练的同一网络，最佳结果在测试中达到了77％的F1评分，增量为9％和12％。
Nodule malignancy assessment is a complex, time-consuming and error-prone task. Current clinical practice requires measuring changes in size and density of the nodule at different time-points. State of the art solutions rely on 3D convolutional neural networks built on pulmonary nodules obtained from single CT scan per patient. In this work, we propose a two-stream 3D convolutional neural network that predicts malignancy by jointly analyzing two pulmonary nodule volumes from the same patient taken at different time-points. Best results achieve 77% of F1-score in test with an increment of 9% and 12% of F1-score with respect to the same network trained with images from a single time-point.
