Patch-based Medical Image Segmentation using Matrix Product State Tensor Networks
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Patch-based Medical Image Segmentation using Matrix Product State Tensor Networks. / Selvan, Raghavendra; Dam, Erik B; Flensborg, Søren Alexander; Petersen, Jens.
I: The Journal of Machine Learning for Biomedical Imaging, Bind 2022, 005, 2022, s. 1-24.Publikation: Bidrag til tidsskrift › Tidsskriftartikel › Forskning › fagfællebedømt
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TY - JOUR
T1 - Patch-based Medical Image Segmentation using Matrix Product State Tensor Networks
AU - Selvan, Raghavendra
AU - Dam, Erik B
AU - Flensborg, Søren Alexander
AU - Petersen, Jens
N1 - Possible journal extension of our preliminary conference work "Segmenting two-dimensional structures with strided tensor networks", Selvan et al. 2021, available at arXiv:2102.06900. 22 pages, 12 figures
PY - 2022
Y1 - 2022
N2 - Tensor networks are efficient factorisations of high dimensional tensors into network of lower order tensors. They have been most commonly used to model entanglement in quantum many-body systems and more recently are witnessing increased applications in supervised machine learning. In this work, we formulate image segmentation in a supervised setting with tensor networks. The key idea is to first lift the pixels in image patches to exponentially high dimensional feature spaces and using a linear decision hyper-plane to classify the input pixels into foreground and background classes. The high dimensional linear model itself is approximated using the matrix product state (MPS) tensor network. The MPS is weight-shared between the non-overlapping image patches resulting in our strided tensor network model. The performance of the proposed model is evaluated on three three 2D- and one 3D- biomedical imaging datasets. The performance of the proposed tensor network segmentation model is compared with relevant baseline methods. In the 2D experiments, the tensor network model yeilds competitive performance compared to the baseline methods while being more resource efficient.
AB - Tensor networks are efficient factorisations of high dimensional tensors into network of lower order tensors. They have been most commonly used to model entanglement in quantum many-body systems and more recently are witnessing increased applications in supervised machine learning. In this work, we formulate image segmentation in a supervised setting with tensor networks. The key idea is to first lift the pixels in image patches to exponentially high dimensional feature spaces and using a linear decision hyper-plane to classify the input pixels into foreground and background classes. The high dimensional linear model itself is approximated using the matrix product state (MPS) tensor network. The MPS is weight-shared between the non-overlapping image patches resulting in our strided tensor network model. The performance of the proposed model is evaluated on three three 2D- and one 3D- biomedical imaging datasets. The performance of the proposed tensor network segmentation model is compared with relevant baseline methods. In the 2D experiments, the tensor network model yeilds competitive performance compared to the baseline methods while being more resource efficient.
KW - cs.CV
U2 - 10.48550/arXiv.2109.07138
DO - 10.48550/arXiv.2109.07138
M3 - Journal article
VL - 2022
SP - 1
EP - 24
JO - The Journal of Machine Learning for Biomedical Imaging
JF - The Journal of Machine Learning for Biomedical Imaging
SN - 2766-905X
M1 - 005
ER -
ID: 279885138