Optimized Activation for Quantum-Inspired Self-supervised Neural Network
based Fully Automated Brain MR Image Segmentation
Abstract
The slow-convergence problem degrades the segmentation performance of
the recently proposed Quantum-Inspired Self-supervised Neural Network
models owing to lack of suitable tailoring of the inter-connection
weights. Hence, incorporation of quantum-inspired meta-heuristics in the
Quantum-Inspired Self-supervised Neural Network models optimizes their
hyper-parameters and inter-connection weights. This paper is aimed at
proposing an optimized version of a Quantum-Inspired Self-supervised
Neural Network (QIS-Net) model for optimal
segmentation of brain Magnetic Resonance (MR) Imaging. The suggested
Optimized Quantum-Inspired Self-supervised Neural Network (Opti-QISNet)
model resembles the architecture of QIS-Net and its operations are
leveraged to obtain optimal segmentation outcome. The optimized
activation function employed in the presented model is referred to as
Quantum-Inspired Optimized Multi-Level Sigmoidal (Opti-QSig) activation.
The Opti-QSig activation function is optimized by three quantum-inspired
meta-heuristics with fifitness evaluation using Otsu’s multi-level
thresholding. Rigorous experiments have been conducted on Dynamic
Susceptibility Contrast (DSC) brain MR images from Nature data
repository. The experimental outcomes show that the proposed
self-supervised Opti-QISNet model offffers a promising alternative to
the deeply supervised neural network based architectures (UNet and
FCNNs) in medical image segmentation and outperforms our recently
developed models QIBDS Net and QIS-Net.