Locally orderless tensor networks for classifying two- and three-dimensional medical images

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Locally orderless tensor networks for classifying two- and three-dimensional medical images. / Selvan, Raghavendra; Ørting, Silas; Dam, Erik B.

In: The Journal of Machine Learning for Biomedical Imaging, Vol. 5, No. SI, 2021, p. 1-21.

Research output: Contribution to journal › Conference article › Research › peer-review

Harvard

Selvan, R, Ørting, S & Dam, EB 2021, 'Locally orderless tensor networks for classifying two- and three-dimensional medical images', The Journal of Machine Learning for Biomedical Imaging, vol. 5, no. SI, pp. 1-21.

APA

Selvan, R., Ørting, S., & Dam, E. B. (2021). Locally orderless tensor networks for classifying two- and three-dimensional medical images. The Journal of Machine Learning for Biomedical Imaging, 5(SI), 1-21.

Vancouver

Selvan R, Ørting S, Dam EB. Locally orderless tensor networks for classifying two- and three-dimensional medical images. The Journal of Machine Learning for Biomedical Imaging. 2021;5(SI):1-21.

Author

Selvan, Raghavendra ; Ørting, Silas ; Dam, Erik B. / Locally orderless tensor networks for classifying two- and three-dimensional medical images. In: The Journal of Machine Learning for Biomedical Imaging. 2021 ; Vol. 5, No. SI. pp. 1-21.

Bibtex

@inproceedings{27df8677e7b6461f809a222144b860d5,
title = "Locally orderless tensor networks for classifying two- and three-dimensional medical images",
abstract = "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.",
keywords = "cs.CV, cs.LG, stat.ML",
author = "Raghavendra Selvan and Silas {\O}rting and Dam, {Erik B}",
note = "Source code will be available at https://github.com/raghavian/LoTeNet_pytorch/; MIDL 2020 : International Conference on Medical Imaging with Deep Learning ; Conference date: 06-07-2020 Through 08-07-2020",
year = "2021",
language = "English",
volume = "5",
pages = "1--21",
journal = "The Journal of Machine Learning for Biomedical Imaging",
issn = "2766-905X",
number = "SI",

}

RIS

TY - GEN

T1 - Locally orderless tensor networks for classifying two- and three-dimensional medical images

AU - Selvan, Raghavendra

AU - Ørting, Silas

AU - Dam, Erik B

N1 - Source code will be available at https://github.com/raghavian/LoTeNet_pytorch/

PY - 2021

Y1 - 2021

N2 - 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.

AB - 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.

KW - cs.CV

KW - cs.LG

KW - stat.ML

M3 - Conference article

VL - 5

SP - 1

EP - 21

JO - The Journal of Machine Learning for Biomedical Imaging

JF - The Journal of Machine Learning for Biomedical Imaging

SN - 2766-905X

IS - SI

T2 - MIDL 2020 : International Conference on Medical Imaging with Deep Learning

Y2 - 6 July 2020 through 8 July 2020

ER -

ID: 249297561