Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource Articles Article Faculty Publications -Articles Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource Qutrit-Inspired Fully Self-Supervised Shallow Quantum Learning Network for Brain Tumor Segmentation Subject The topic of the resource Magnetic Resonance (MR) image segmentation; QIS-Net; quantum computing; qutrit; U-Net and URes-Net Description An account of the resource Classical 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. 2012 IEEE. Creator An entity primarily responsible for making the resource Konar D.; Bhattacharyya S.; Panigrahi B.K.; Behrman E.C. Source A related resource from which the described resource is derived IEEE Transactions on Neural Networks and Learning Systems, Vol-33, No. 11, pp. 6331-6345. Publisher An entity responsible for making the resource available Institute of Electrical and Electronics Engineers Inc. Date A point or period of time associated with an event in the lifecycle of the resource 2022-01-01 Identifier An unambiguous reference to the resource within a given context <a href="https://doi.org/10.1109/TNNLS.2021.3077188" target="_blank" rel="noreferrer noopener">https://doi.org/10.1109/TNNLS.2021.3077188</a> <br /><br /><a href="https://www.scopus.com/inward/record.uri?eid=2-s2.0-85105888289&amp;doi=10.1109%2FTNNLS.2021.3077188&amp;partnerID=40&amp;md5=54586ec999a03fe2e877f3c414a12a6c" target="_blank" rel="noreferrer noopener">https://www.scopus.com/inward/record.uri?eid=2-s2.0-85105888289&amp;doi=10.1109%2fTNNLS.2021.3077188&amp;partnerID=40&amp;md5=54586ec999a03fe2e877f3c414a12a6c</a> Rights Information about rights held in and over the resource All Open Access; Green Open Access Relation A related resource ISSN: 2162237X; PubMed ID: 33983887 Format The file format, physical medium, or dimensions of the resource Online Language A language of the resource English Type The nature or genre of the resource Article Coverage The spatial or temporal topic of the resource, the spatial applicability of the resource, or the jurisdiction under which the resource is relevant Konar D., IIT Delhi, Department of Electrical Engineering, New Delhi, 110016, India, Sikkim Manipal Institute of Technology, Department of Computer Science and Engineering, Sikkim, 737136, India; Bhattacharyya S., CHRIST (Deemed to be University), Department of Computer Science and Engineering, Bengaluru, 560029, India; Panigrahi B.K., IIT Delhi, Department of Electrical Engineering, New Delhi, 110016, India; Behrman E.C., Wichita State University, Department of Mathematics and Physics, Wichita, 67260, KS, United States