Obtaining ground truth data in medical imaging has difficulties due to the fact that it requires a lot of annotating time from the experts in the field. Also, when trained with supervised learning, it detects only the cases included in the labels. In real practice, we want to also open to other possibilities than the named cases while examining the medical images. As a solution, the need for anomaly detection that can detect and localize abnormalities by learning the normal characteristics using only normal images is emerging. With medical image data, we can design either 2D or 3D networks of self-supervised learning for anomaly detection task. Although 3D networks, which learns 3D structures of the human body, show good performance in 3D medical image anomaly detection, they cannot be stacked in deeper layers due to memory problems. While 2D networks have advantage in feature detection, they lack 3D context information. In this paper, we develop a method for combining the strength of the 3D network and the strength of the 2D network through joint embedding. We also propose the pretask of self-supervised learning to make it possible for the networks to learn efficiently. Through the experiments, we show that the proposed method achieves better performance in both classification and segmentation tasks compared to the SoTA method.
翻译:在医学成像中获取地面真相数据有困难,因为需要实地专家提供大量时间说明医学成像中的地面真相数据,因为需要大量时间从医学成像中获取实地真相数据,此外,在接受监督学习培训时,它只检测标签中包含的病例。在实际操作中,我们想在检查医疗图象时,也向指定案例以外的其他可能性开放。作为一种解决办法,正在出现通过只使用正常图像学习正常特征来检测和定位异常现象的需要。有了医学成像数据,我们可以设计2D或3D自我监督学习网络,以完成异常检测任务。虽然3D网络学习人体的3D结构,在3D医疗图象异常探测中表现出良好的表现,但由于记忆问题,它们不能堆积在更深层中。虽然2D网络在特征检测方面有优势,但缺乏3D背景信息。在本文中,我们开发了一种方法,将3D网络的实力和2D网络的实力通过联合嵌入结合起来。我们还提议了自我监督学习的预设任务,使网络能够高效率地学习TA任务。我们通过实验展示了两种方法,从而取得更好的技术。