论文标题
通过转移学习的关节肝病变细分和分类
Joint Liver Lesion Segmentation and Classification via Transfer Learning
论文作者
论文摘要
转移学习和联合学习方法被广泛用于改善卷积神经网络（CNN）的性能。在目标数据集通常很小的医学成像应用中，转移学习改善了特征学习，而联合学习表现出在改善网络的概括和鲁棒性方面的有效性。在这项工作中，我们研究了这两种方法的结合，以解决肝病变量分割和分类问题。为此，评估了三种病变类型的病变分割和分类的332个腹部CT切片。对于特征学习，使用了MICCAI 2017肝肿瘤分割（LITS）挑战的数据集。联合学习均显示了分割和分类结果的改善。我们表明，一个简单的联合框架的表现优于常用的多任务架构（Y-NET），与Y-NET相比提高了3％，分类精度的提高了10％。
Transfer learning and joint learning approaches are extensively used to improve the performance of Convolutional Neural Networks (CNNs). In medical imaging applications in which the target dataset is typically very small, transfer learning improves feature learning while joint learning has shown effectiveness in improving the network's generalization and robustness. In this work, we study the combination of these two approaches for the problem of liver lesion segmentation and classification. For this purpose, 332 abdominal CT slices containing lesion segmentation and classification of three lesion types are evaluated. For feature learning, the dataset of MICCAI 2017 Liver Tumor Segmentation (LiTS) Challenge is used. Joint learning shows improvement in both segmentation and classification results. We show that a simple joint framework outperforms the commonly used multi-task architecture (Y-Net), achieving an improvement of 10% in classification accuracy, compared to a 3% improvement with Y-Net.
