论文标题
2D和3D网络的关节嵌入医疗图像异常检测
Joint Embedding of 2D and 3D Networks for Medical Image Anomaly Detection
论文作者
论文摘要
在医学成像中获取地面真实数据是因为它需要该领域专家的大量注释时间。另外，当接受有监督的学习培训时，它仅检测标签中包含的案例。在实际实践中，我们还希望在检查医学图像时对其他可能性开放。作为解决方案，仅使用正常图像学习正常特征来检测和定位异常的异常检测需要。借助医疗图像数据，我们可以设计自我监督学习的2D或3D网络，以实现异常检测任务。尽管学习人体的3D结构的3D网络在3D医疗图像异常检测中表现出良好的性能，但由于记忆问题，它们无法在更深的层中堆叠。尽管2D网络在功能检测方面具有优势，但它们缺乏3D上下文信息。在本文中，我们开发了一种通过关节嵌入结合3D网络强度和2D网络强度的方法。我们还提出了自我监督的学习的前提，以使网络有效地学习成为可能。通过实验，我们表明，与SOTA方法相比，所提出的方法在分类和分割任务中都能达到更好的性能。
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.
