Tensor networks provide an efficient approximation of operations involving high dimensional tensors and have been extensively used in modelling quantum many-body systems. More recently, supervised learning has been attempted with tensor networks, primarily focused on tasks such as image classification. In this work, we propose a novel formulation of tensor networks for supervised image segmentation which allows them to operate on high resolution medical images. We use the matrix product state (MPS) tensor network on non-overlapping patches of a given input image to predict the segmentation mask by learning a pixel-wise linear classification rule in a high dimensional space. The proposed model is end-to-end trainable using backpropagation. It is implemented as a Strided Tensor Network to reduce the parameter complexity. The performance of the proposed method is evaluated on two public medical imaging datasets and compared to relevant baselines. The evaluation shows that the strided tensor network yields competitive performance compared to CNN-based models while using fewer resources. Additionally, based on the experiments we discuss the feasibility of using fully linear models for segmentation tasks.
翻译:电锯网络对涉及高维抗体的操作提供了有效的近似效果,并被广泛用于量子多体系统的建模中。最近,还尝试了与电压网络的监督下学习,主要侧重于图像分类等任务。在这项工作中,我们提议了一种用于监督图像分解的强压网络的新式配方,使其能够在高分辨率医学图像上运行。我们用矩阵产品状态(MPS)强力网络对特定输入图像的不重叠补补补点进行预测,在高维空间学习像素的线性线性分类规则。提议的模式是利用反向转换进行终端到终端培训的模型。它作为Straded Tensor网络实施,以减少参数复杂性。拟议方法的性能根据两个公共医学成像数据集和相关基线进行了评估。我们利用较少的资源,使用CNN模型来显示结构型高压强的网络具有竞争性性能。此外,根据我们讨论使用完全线性模型进行分解任务的可行性的实验。