论文标题
内部：3D颅内动脉瘤数据集用于深度学习
IntrA: 3D Intracranial Aneurysm Dataset for Deep Learning
论文作者
论文摘要
医学是深度学习模型的重要应用领域。该领域的研究是医学专业知识和数据科学知识的结合。在本文中，我们引入了一个开放式3D颅内动脉瘤数据集，它使得可用的基于点和网格的分类和分割模型，而不是2D医学图像。我们的数据集可用于诊断颅内动脉瘤，并提取颈部以进行医学和其他深度学习方面的剪裁操作，例如正常的估计和表面重建。我们通过测试最先进的网络提供了分类和部分细分的大规模基准。我们还讨论了每种方法的性能，并演示了我们数据集的挑战。可以在此处访问已发布的数据集：https：//github.com/intra3d2019/intra。
Medicine is an important application area for deep learning models. Research in this field is a combination of medical expertise and data science knowledge. In this paper, instead of 2D medical images, we introduce an open-access 3D intracranial aneurysm dataset, IntrA, that makes the application of points-based and mesh-based classification and segmentation models available. Our dataset can be used to diagnose intracranial aneurysms and to extract the neck for a clipping operation in medicine and other areas of deep learning, such as normal estimation and surface reconstruction. We provide a large-scale benchmark of classification and part segmentation by testing state-of-the-art networks. We also discuss the performance of each method and demonstrate the challenges of our dataset. The published dataset can be accessed here: https://github.com/intra3d2019/IntrA.
