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  • TNNLS_2021_P_18280

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3-D Quantum-Inspired Self-Supervised Tensor Network for Volumetric Segmentation of Medical Images

Research output: Contribution to Journal/MagazineJournal articlepeer-review

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Standard

3-D Quantum-Inspired Self-Supervised Tensor Network for Volumetric Segmentation of Medical Images. / Konar, Debanjan; Bhattacharyya, Siddhartha; Gandhi, Tapan K. et al.
In: IEEE Transactions on Neural Networks and Learning Systems, 06.02.2023.

Research output: Contribution to Journal/MagazineJournal articlepeer-review

Harvard

Konar, D, Bhattacharyya, S, Gandhi, TK, Panigrahi, BK & Jiang, R 2023, '3-D Quantum-Inspired Self-Supervised Tensor Network for Volumetric Segmentation of Medical Images', IEEE Transactions on Neural Networks and Learning Systems. https://doi.org/10.1109/TNNLS.2023.3240238

APA

Konar, D., Bhattacharyya, S., Gandhi, T. K., Panigrahi, B. K., & Jiang, R. (2023). 3-D Quantum-Inspired Self-Supervised Tensor Network for Volumetric Segmentation of Medical Images. IEEE Transactions on Neural Networks and Learning Systems. Advance online publication. https://doi.org/10.1109/TNNLS.2023.3240238

Vancouver

Konar D, Bhattacharyya S, Gandhi TK, Panigrahi BK, Jiang R. 3-D Quantum-Inspired Self-Supervised Tensor Network for Volumetric Segmentation of Medical Images. IEEE Transactions on Neural Networks and Learning Systems. 2023 Feb 6. Epub 2023 Feb 6. doi: 10.1109/TNNLS.2023.3240238

Author

Konar, Debanjan ; Bhattacharyya, Siddhartha ; Gandhi, Tapan K. et al. / 3-D Quantum-Inspired Self-Supervised Tensor Network for Volumetric Segmentation of Medical Images. In: IEEE Transactions on Neural Networks and Learning Systems. 2023.

Bibtex

@article{e774dec4ce7f4edab60a013e7d012917,
title = "3-D Quantum-Inspired Self-Supervised Tensor Network for Volumetric Segmentation of Medical Images",
abstract = "This article introduces a novel shallow 3-D self-supervised tensor neural network in quantum formalism for volumetric segmentation of medical images with merits of obviating training and supervision. The proposed network is referred to as the 3-D quantum-inspired self-supervised tensor neural network (3-D-QNet). The underlying architecture of 3-D-QNet is composed of a trinity of volumetric layers, viz., input, intermediate, and output layers interconnected using an S -connected third-order neighborhood-based topology for voxelwise processing of 3-D medical image data, suitable for semantic segmentation. Each of the volumetric layers contains quantum neurons designated by qubits or quantum bits. The incorporation of tensor decomposition in quantum formalism leads to faster convergence of network operations to preclude the inherent slow convergence problems faced by the classical supervised and self-supervised networks. The segmented volumes are obtained once the network converges. The suggested 3-D-QNet is tailored and tested on the BRATS 2019 Brain MR image dataset and the Liver Tumor Segmentation Challenge (LiTS17) dataset extensively in our experiments. The 3-D-QNet has achieved promising dice similarity (DS) as compared with the time-intensive supervised convolutional neural network (CNN)-based models, such as 3-D-UNet, voxelwise residual network (VoxResNet), Dense-Res-Inception Net (DRINet), and 3-D-ESPNet, thereby showing a potential advantage of our self-supervised shallow network on facilitating semantic segmentation.",
author = "Debanjan Konar and Siddhartha Bhattacharyya and Gandhi, {Tapan K.} and Panigrahi, {Bijaya K.} and Richard Jiang",
note = "{\textcopyright}2023 IEEE. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE. ",
year = "2023",
month = feb,
day = "6",
doi = "10.1109/TNNLS.2023.3240238",
language = "English",
journal = "IEEE Transactions on Neural Networks and Learning Systems",
issn = "2162-237X",
publisher = "IEEE Computational Intelligence Society",

}

RIS

TY - JOUR

T1 - 3-D Quantum-Inspired Self-Supervised Tensor Network for Volumetric Segmentation of Medical Images

AU - Konar, Debanjan

AU - Bhattacharyya, Siddhartha

AU - Gandhi, Tapan K.

AU - Panigrahi, Bijaya K.

AU - Jiang, Richard

N1 - ©2023 IEEE. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE.

PY - 2023/2/6

Y1 - 2023/2/6

N2 - This article introduces a novel shallow 3-D self-supervised tensor neural network in quantum formalism for volumetric segmentation of medical images with merits of obviating training and supervision. The proposed network is referred to as the 3-D quantum-inspired self-supervised tensor neural network (3-D-QNet). The underlying architecture of 3-D-QNet is composed of a trinity of volumetric layers, viz., input, intermediate, and output layers interconnected using an S -connected third-order neighborhood-based topology for voxelwise processing of 3-D medical image data, suitable for semantic segmentation. Each of the volumetric layers contains quantum neurons designated by qubits or quantum bits. The incorporation of tensor decomposition in quantum formalism leads to faster convergence of network operations to preclude the inherent slow convergence problems faced by the classical supervised and self-supervised networks. The segmented volumes are obtained once the network converges. The suggested 3-D-QNet is tailored and tested on the BRATS 2019 Brain MR image dataset and the Liver Tumor Segmentation Challenge (LiTS17) dataset extensively in our experiments. The 3-D-QNet has achieved promising dice similarity (DS) as compared with the time-intensive supervised convolutional neural network (CNN)-based models, such as 3-D-UNet, voxelwise residual network (VoxResNet), Dense-Res-Inception Net (DRINet), and 3-D-ESPNet, thereby showing a potential advantage of our self-supervised shallow network on facilitating semantic segmentation.

AB - This article introduces a novel shallow 3-D self-supervised tensor neural network in quantum formalism for volumetric segmentation of medical images with merits of obviating training and supervision. The proposed network is referred to as the 3-D quantum-inspired self-supervised tensor neural network (3-D-QNet). The underlying architecture of 3-D-QNet is composed of a trinity of volumetric layers, viz., input, intermediate, and output layers interconnected using an S -connected third-order neighborhood-based topology for voxelwise processing of 3-D medical image data, suitable for semantic segmentation. Each of the volumetric layers contains quantum neurons designated by qubits or quantum bits. The incorporation of tensor decomposition in quantum formalism leads to faster convergence of network operations to preclude the inherent slow convergence problems faced by the classical supervised and self-supervised networks. The segmented volumes are obtained once the network converges. The suggested 3-D-QNet is tailored and tested on the BRATS 2019 Brain MR image dataset and the Liver Tumor Segmentation Challenge (LiTS17) dataset extensively in our experiments. The 3-D-QNet has achieved promising dice similarity (DS) as compared with the time-intensive supervised convolutional neural network (CNN)-based models, such as 3-D-UNet, voxelwise residual network (VoxResNet), Dense-Res-Inception Net (DRINet), and 3-D-ESPNet, thereby showing a potential advantage of our self-supervised shallow network on facilitating semantic segmentation.

U2 - 10.1109/TNNLS.2023.3240238

DO - 10.1109/TNNLS.2023.3240238

M3 - Journal article

JO - IEEE Transactions on Neural Networks and Learning Systems

JF - IEEE Transactions on Neural Networks and Learning Systems

SN - 2162-237X

ER -