QIBDS Net: A Quantum-Inspired Bi-Directional Self-supervised Neural Network Architecture for Automatic Brain MR Image Segmentation

Konar, Debanjan; Bhattacharyya, Siddhartha; Panigrahi, Bijaya Ketan

doi:10.1007/978-3-030-34872-4_10

Cited by 7 publications

(14 citation statements)

References 12 publications

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“…al [12,13] obviates the quantum-back-propagation algorithm and resorts to counter-propagation of network patterns. Recently, Konar et al suggested few quantum-inspired neural network models [16,17] for fully self-supervised brain MR image segmentation. These quantum-inspired selfsupervised neural network models suffer from relatively slow convergence problems while applied on brain MR images with large variations of gray-scales.…”

Section: Related Workmentioning

confidence: 99%

“…In this proposed Opti-QISNet model, the Quantum-Inspired Self-supervised Neural Network (QISNet) architecture [16,17] is characterized by a novel quantuminspired optimized multi-level sigmoidal (Opti-QSig) activation function suitable for optimal adaptive thresholding of MR images, thereby enabling precise multi-level segmentation. The basic architecture of the Opti-QISNet mimics the QISNet architecture and the trinity layers of the Opti-QISNet architecture are arranged as input, hidden or intermediate and output comprising quantum neurons (qubits) as shown in Figure 1.…”

Section: Optimized Quantum-inspired Self-supervised Neural Network (Omentioning

confidence: 99%

“…Moreover, QINN models fall short of the multi-level activation function and consequently, they fail to produce optimal thresholding for multi-level images. Recently, quantuminspired neural networks referred to as QIBDS-Net and QIS-Net architectures are suggested by Konar et al [16,17] for fully automated brain MR image segmentation. QIBDS-Net [16] and QIS-Net [17] fail to provide optimal outcome owing to wide variation of gray-scales in brain MR images and often suffer from convergence problems.…”

Section: Introductionmentioning

confidence: 99%

“…Recently, quantuminspired neural networks referred to as QIBDS-Net and QIS-Net architectures are suggested by Konar et al [16,17] for fully automated brain MR image segmentation. QIBDS-Net [16] and QIS-Net [17] fail to provide optimal outcome owing to wide variation of gray-scales in brain MR images and often suffer from convergence problems. The key aim of this current work is to develop an optimized version of our previously developed quantum-inspired self-supervised neural network models [16,17] for fully automated medical image segmentation without any form supervision or training.…”

Section: Introductionmentioning

confidence: 99%

“…QIBDS-Net [16] and QIS-Net [17] fail to provide optimal outcome owing to wide variation of gray-scales in brain MR images and often suffer from convergence problems. The key aim of this current work is to develop an optimized version of our previously developed quantum-inspired self-supervised neural network models [16,17] for fully automated medical image segmentation without any form supervision or training. The significant contributions of the proposed work are as follows.…”

Section: Introductionmentioning

confidence: 99%

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Optimized Activation for Quantum-Inspired Self-supervised Neural Network based Fully Automated Brain MR Image Segmentation

Konar¹,

Bhattacharyya²,

Panigrahi³

2020

Preprint

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<div>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</div><div>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.</div>

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Section: Related Workmentioning

confidence: 99%

Section: Optimized Quantum-inspired Self-supervised Neural Network (Omentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Optimized Activation for Quantum-Inspired Self-supervised Neural Network based Fully Automated Brain MR Image Segmentation

Konar¹,

Bhattacharyya²,

Panigrahi³

2020

Preprint

Self Cite

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Comparative Study on Architecture of Deep Neural Networks for Segmentation of Brain Tumor using Magnetic Resonance Images

Preetha,

Priyadarsini,

Nisha

2023

IEEE Access

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The state-of-the-art works for the segmentation of brain tumor using the images acquired by Magnetic Resonance Imaging (MRI) with their performances are analyzed in this comparative study. First, the architectures of convolutional neural networks (CNN) and the variants of U-shaped Network (U-Net), a kind of Deep Neural Network (DNN) are compared and their differences are highlighted. The publicly available datasets of MRI images specifically Brain Tumor Segmentation (BraTS) are also discussed. Next, the performances of tumor segmentation of various methods in the literature are compared using the parameters such as Dice score and Hausdroff distance ( 95). This study concludes that the U-Net based architectures using the BraTS-2019 dataset outperform well compared with other CNN based architectures.

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Qutrit-Inspired Fully Self-Supervised Shallow Quantum Learning Network for Brain Tumor Segmentation

Konar

Bhattacharyya

Panigrahi

et al. 2022

IEEE Trans. Neural Netw. Learning Syst.

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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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QIBDS Net: A Quantum-Inspired Bi-Directional Self-supervised Neural Network Architecture for Automatic Brain MR Image Segmentation

Cited by 7 publications

References 12 publications

Optimized Activation for Quantum-Inspired Self-supervised Neural Network based Fully Automated Brain MR Image Segmentation

Optimized Activation for Quantum-Inspired Self-supervised Neural Network based Fully Automated Brain MR Image Segmentation

Comparative Study on Architecture of Deep Neural Networks for Segmentation of Brain Tumor using Magnetic Resonance Images

Qutrit-Inspired Fully Self-Supervised Shallow Quantum Learning Network for Brain Tumor Segmentation

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