TY - GEN

T1 - Segmenting Two-Dimensional Structures with Strided Tensor Networks

AU - Selvan, Raghavendra

AU - Dam, Erik B.

AU - Petersen, Jens

N1 - Publisher Copyright: © 2021, Springer Nature Switzerland AG.

PY - 2021

Y1 - 2021

N2 - 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.(Source code: https://github.com/raghavian/strided-tenet )

AB - 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.(Source code: https://github.com/raghavian/strided-tenet )

KW - Image segmentation

KW - Linear models

KW - Tensor networks

U2 - 10.1007/978-3-030-78191-0_31

DO - 10.1007/978-3-030-78191-0_31

M3 - Article in proceedings

AN - SCOPUS:85111469288

SN - 9783030781903

T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

SP - 401

EP - 414

BT - Information Processing in Medical Imaging - 27th International Conference, IPMI 2021, Proceedings

A2 - Feragen, Aasa

A2 - Sommer, Stefan

A2 - Schnabel, Julia

A2 - Nielsen, Mads

PB - Springer

T2 - 27th International Conference on Information Processing in Medical Imaging, IPMI 2021

Y2 - 28 June 2021 through 30 June 2021

ER -