1 research outputs found
Qutrit-inspired Fully Self-supervised Shallow Quantum Learning Network for Brain Tumor Segmentation
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