论文标题
使用周期一致的对抗结构域适应的感应传递学习方法，并应用于脑肿瘤分段
An Inductive Transfer Learning Approach using Cycle-consistent Adversarial Domain Adaptation with Application to Brain Tumor Segmentation
论文作者
论文摘要
随着医学图像分析应用程序的监督机器学习的最新进展，各种域的注释的医学图像数据集被广泛共享。鉴于注释标签需要医学专业知识，因此应将此类标签应用于尽可能多的学习任务。但是，每个带注释的图像的多模式性质使得很难在不同任务之间共享注释标签。在这项工作中，我们提供了一种归纳转移学习（ITL）方法，以使用基于自行车gan的无监督域适应（UDA）（UDA）来采用源域数据集的注释标签对目标域数据集的任务。为了评估ITL方法的适用性，我们在磁共振成像（MRI）图像的源结构域数据集（MRI）图像上采用了脑组织注释标签，用于MRI的目标域数据集对脑肿瘤分割的任务。结果证实，脑肿瘤分割的分割准确性显着提高。提出的ITL方法可以为医学图像分析领域做出重大贡献，因为我们开发了一种基本工具来使用医学图像改进和促进各种任务。
With recent advances in supervised machine learning for medical image analysis applications, the annotated medical image datasets of various domains are being shared extensively. Given that the annotation labelling requires medical expertise, such labels should be applied to as many learning tasks as possible. However, the multi-modal nature of each annotated image renders it difficult to share the annotation label among diverse tasks. In this work, we provide an inductive transfer learning (ITL) approach to adopt the annotation label of the source domain datasets to tasks of the target domain datasets using Cycle-GAN based unsupervised domain adaptation (UDA). To evaluate the applicability of the ITL approach, we adopted the brain tissue annotation label on the source domain dataset of Magnetic Resonance Imaging (MRI) images to the task of brain tumor segmentation on the target domain dataset of MRI. The results confirm that the segmentation accuracy of brain tumor segmentation improved significantly. The proposed ITL approach can make significant contribution to the field of medical image analysis, as we develop a fundamental tool to improve and promote various tasks using medical images.
