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

TL;DR

This paper introduces a novel qutrit-inspired quantum neural network for brain tumor segmentation that improves accuracy and reduces the need for supervision and computational resources.

Contribution

It presents the first qutrit-based self-supervised quantum neural network model for medical image segmentation, enhancing convergence and accuracy over classical and existing quantum models.

Findings

Achieved high dice similarity scores in tumor segmentation

Outperformed classical U-Net and URes-Net models

Reduced computational resources and human intervention

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.