论文标题
本地无秩序的无量张量网络，用于对二维医学图像进行分类
Locally orderless tensor networks for classifying two- and three-dimensional medical images
论文作者
论文摘要
张量网络是将高等级张量分解为较低等级张量网络的网络，并且主要用于分析量子多体问题。张量网络已经看到了最近对监督学习任务的关注，重点是图像分类。在这项工作中，我们改进了矩阵乘积状态（MPS）张量网络，该网络可以在一维矢量上运行，可用于使用2D和3D医疗图像。我们将小图像区域视为无秩序的区域，将其空间信息挤入特征维度，然后对这些本地无秩序的区域进行MPS操作。然后以层次方式汇总这些局部表示以保留全球结构。将提出的本地无序张量网络（Lotenet）与三个数据集上的相关方法进行了比较。 LoteNet的架构在所有实验中都固定，我们表明它需要较少的计算资源才能在PAR或比较方法上获得绩效。
Tensor networks are factorisations of high rank tensors into networks of lower rank tensors and have primarily been used to analyse quantum many-body problems. Tensor networks have seen a recent surge of interest in relation to supervised learning tasks with a focus on image classification. In this work, we improve upon the matrix product state (MPS) tensor networks that can operate on one-dimensional vectors to be useful for working with 2D and 3D medical images. We treat small image regions as orderless, squeeze their spatial information into feature dimensions and then perform MPS operations on these locally orderless regions. These local representations are then aggregated in a hierarchical manner to retain global structure. The proposed locally orderless tensor network (LoTeNet) is compared with relevant methods on three datasets. The architecture of LoTeNet is fixed in all experiments and we show it requires lesser computational resources to attain performance on par or superior to the compared methods.
