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dc.contributor.authorKonar, Debanjan
dc.contributor.authorBhattacharyya, Siddhartha
dc.contributor.authorPanigrahi, Bijaya K.
dc.contributor.authorBehrman, Elizabeth C.
dc.date.accessioned2021-06-01T03:43:55Z
dc.date.available2021-06-01T03:43:55Z
dc.date.issued2021-05-13
dc.identifier.citationKonar, D., Bhattacharyya, S., Panigrahi, B. K., & Behrman, E. C. (2021). Qutrit-inspired fully self-supervised shallow quantum learning network for brain tumor segmentation. IEEE Transactions on Neural Networks and Learning Systems, doi:10.1109/TNNLS.2021.3077188en_US
dc.identifier.issn2162-237X
dc.identifier.issn2162-2388
dc.identifier.urihttps://doi.org/10.1109/TNNLS.2021.3077188
dc.identifier.urihttps://soar.wichita.edu/handle/10057/20079
dc.descriptionOpen Access ( Personal use is permitted, but republication/redistribution requires IEEE permission. See https://www.ieee.org/publications/rights/index.html for more information.)en_US
dc.description.abstractClassical self-supervised networks suffer from convergence problems and reduced segmentation accuracy due to forceful termination. Qubits or bilevel quantum bits often describe quantum neural network models. In this article, a novel self-supervised shallow learning network model exploiting the sophisticated three-level qutrit-inspired quantum information system, referred to as quantum fully self-supervised neural network (QFS-Net), is presented for automated segmentation of brain magnetic resonance (MR) images. The QFS-Net model comprises a trinity of a layered structure of qutrits interconnected through parametric Hadamard gates using an eight-connected second-order neighborhood-based topology. The nonlinear transformation of the qutrit states allows the underlying quantum neural network model to encode the quantum states, thereby enabling a faster self-organized counterpropagation of these states between the layers without supervision. The suggested QFS-Net model is tailored and extensively validated on the Cancer Imaging Archive (TCIA) dataset collected from the Nature repository. The experimental results are also compared with state-of-the-art supervised (U-Net and URes-Net architectures) and the self-supervised QIS-Net model and its classical counterpart. Results shed promising segmented outcomes in detecting tumors in terms of dice similarity and accuracy with minimum human intervention and computational resources. The proposed QFS-Net is also investigated on natural gray-scale images from the Berkeley segmentation dataset and yields promising outcomes in segmentation, thereby demonstrating the robustness of the QFS-Net model.en_US
dc.language.isoen_USen_US
dc.publisherInstitute of Electrical and Electronics Engineersen_US
dc.relation.ispartofseriesIEEE Transactions on Neural Networks and Learning Systems;
dc.subjectQuantum computingen_US
dc.subjectImage segmentationen_US
dc.subjectBiological neural networksen_US
dc.subjectComputational modelingen_US
dc.subjectQuantum stateen_US
dc.subjectQubiten_US
dc.subjectQuantum entanglementen_US
dc.titleQutrit-inspired fully self-supervised shallow quantum learning network for brain tumor segmentationen_US
dc.typeArticleen_US
dc.rights.holder© 2021 IEEE. Personal use is permitted, but republication/redistribution requires IEEE permission. See https://www.ieee.org/publications/rights/index.html for more information.en_US


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