标题： 基于集成肿瘤分割和迁移学习的生存预测 显示英文标题

标题： Survival prediction using ensemble tumor segmentation and transfer learning

摘要： 分割肿瘤及其亚区域是一个具有挑战性的任务，如每年的BraTS挑战所证明的那样。 此外，仅使用影像特征来预测患者的生存期，虽然作为评估患者治疗效果的理想结果，但也是一项困难的任务。 在本文中，我们提出了一种级联的流程来分割肿瘤及其亚区域，然后我们结合这些结果以及其他临床特征和来自预训练VGG-16网络的影像特征来预测患者的生存期。 在训练和验证数据集上的初步结果在分割方面显示出有希望的开始，而预测值可以通过在网络特征提取部分进行进一步测试来改进。 显示英文摘要

摘要： Segmenting tumors and their subregions is a challenging task as demonstrated by the annual BraTS challenge. Moreover, predicting the survival of the patient using mainly imaging features, while being a desirable outcome to evaluate the treatment of the patient, it is also a difficult task. In this paper, we present a cascaded pipeline to segment the tumor and its subregions and then we use these results and other clinical features together with image features coming from a pretrained VGG-16 network to predict the survival of the patient. Preliminary results with the training and validation dataset show a promising start in terms of segmentation, while the prediction values could be improved with further testing on the feature extraction part of the network.