Qutrit-inspired Fully Self-supervised Shallow Quantum Learning Network for Brain Tumor Segmentation

• URL: http://arxiv.org/abs/2009.06767v1
• Date: Mon, 14 Sep 2020 22:15:22 GMT
• Title: Qutrit-inspired Fully Self-supervised Shallow Quantum Learning Network for Brain Tumor Segmentation
• Authors: Debanjan Konar, Siddhartha Bhattacharyya, Bijaya K. Panigrahi, and Elizabeth Behrman
• Abstract summary: Qubits or bi-level quantum bits often describe quantum neural network models. In this article, a novel self-supervised shallow learning network model is presented for automated segmentation of brain MR images. Results shed promising segmented outcome in detecting tumors in terms of dice similarity and accuracy with minimum human intervention and computational resources.
• Score: 7.173859338960338
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Classical self-supervised networks suffer from convergence problems and reduced segmentation accuracy due to forceful termination. Qubits or bi-level 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 MR images. The QFS-Net model comprises a trinity of a layered structure of qutrits inter-connected through parametric Hadamard gates using an 8-connected second-order neighborhood-based topology. The non-linear transformation of the qutrit states allows the underlying quantum neural network model to encode the quantum states, thereby enabling a faster self-organized counter-propagation of these states between the layers without supervision. The suggested QFS-Net model is tailored and extensively validated on Cancer Imaging Archive (TCIA) data set collected from Nature repository and also compared with state of the art supervised (U-Net and URes-Net architectures) and the self-supervised QIS-Net model. Results shed promising segmented outcome in detecting tumors in terms of dice similarity and accuracy with minimum human intervention and computational resources.

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