论文标题
“每个人的网”：完全个性化和无监督的神经网络，该网络接受了一名患者的纵向数据训练
'A net for everyone': fully personalized and unsupervised neural networks trained with longitudinal data from a single patient
论文作者
论文摘要
随着个性化医学的重要性，我们训练了个性化神经网络，以检测纵向数据集中的肿瘤进展。在两个数据集上评估了该模型，其中32例被诊断为胶质母细胞瘤多形（GBM）的患者进行了64次扫描。本研究使用了对比增强的脑磁共振成像（MRI）图像的T1W序列。对于每个患者，我们仅使用来自不同时间点的两张图像培训了自己的神经网络。我们的方法使用无监督的网络体系结构的Wasserstein-Gan（生成对抗网络）来绘制这两个图像之间的差异。使用此地图，可以评估肿瘤体积的变化。由于数据增强和网络体系结构的组合，不需要这两个图像的共同注册。此外，我们不依赖任何其他培训数据，（手动）注释或训练前神经网络。该模型的AUC得分为0.87，用于肿瘤变化。我们还引入了修改后的RANO标准，为此，可以实现66％的精度。我们表明，仅使用来自一名患者的数据可用于训练深层神经网络以监测肿瘤的变化。
With the rise in importance of personalized medicine, we trained personalized neural networks to detect tumor progression in longitudinal datasets. The model was evaluated on two datasets with a total of 64 scans from 32 patients diagnosed with glioblastoma multiforme (GBM). Contrast-enhanced T1w sequences of brain magnetic resonance imaging (MRI) images were used in this study. For each patient, we trained their own neural network using just two images from different timepoints. Our approach uses a Wasserstein-GAN (generative adversarial network), an unsupervised network architecture, to map the differences between the two images. Using this map, the change in tumor volume can be evaluated. Due to the combination of data augmentation and the network architecture, co-registration of the two images is not needed. Furthermore, we do not rely on any additional training data, (manual) annotations or pre-training neural networks. The model received an AUC-score of 0.87 for tumor change. We also introduced a modified RANO criteria, for which an accuracy of 66% can be achieved. We show that using data from just one patient can be used to train deep neural networks to monitor tumor change.
