A Quantum-Inspired Self-Supervised Network model for automatic segmentation of brain MR images

Konar, Debanjan; Bhattacharyya, Siddhartha; Gandhi, Tapan Kumar; Panigrahi, Bijaya Ketan

doi:10.1016/j.asoc.2020.106348

Cited by 34 publications

(62 citation statements)

References 25 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Table 1 reports the segmentation results of the proposed PQIS-Net with ResNet50 [9] and 3D-UNet [10] models for three different tasks (infection, lung, infection and lung). It is evident from the experimental data provided in Table 1 and from the statistical significance test (KS test) [19] conducted on the results that in spite of being a self-supervised network, the proposed PQIS-Net attains similar performance in segmentation tasks on the data set [30] in comparison with the pre-trained CNN models (ResNet50 [9] and 3D-UNet [10]) under the four evaluation parameters (ACC, DS, P P V, SS). Table 2 presents the numerical results obtained using the proposed semi-supervised shallow neural network model, ResNet50 [9], 3D-UNet [10], Kang et al [15] and Wang et al [18] for COVID-19 detection on the Brazilian data Figure 6: PQIS-Net segmented lung CT slice#171 [30] with the three different masks.…”

Section: Resultsmentioning

confidence: 94%

Auto-diagnosis of COVID-19 using Lung CT Images with Semi-supervised Shallow Learning Network

Konar

Panigrahi

Bhattacharyya

et al. 2020

Preprint

Self Cite

View full text Add to dashboard Cite

Infection of Novel Coronavirus 2019 (COVID-19) on lung cells and human respiratory systems have raised real concern to the human lives during the current pandemic spread across the world. Recent observations on CT images of human lungs infected by COVID-19 is a challenging task for the researchers in finding suitable image patterns for automatic diagnosis. In this paper, a novel semi-supervised shallow learning network model comprising Parallel Quantum-Inspired Self-supervised Network (PQIS-Net) with Fully Connected (FC) layers is proposed for automatic segmentation followed by patch-based classifications on segmented lung CT images for the diagnosis of COVID-19 disease. The PQIS-Net model is incorporated for fully automated segmentation of lung CT scan images obviating pre-trained convolutional neural network models for feature learning. The PQIS-Net model comprises a trinity of layered structures of quantum bits inter-connected through rotation gates using an 8-connected second-order neighborhood topology for the segmentation of wide variation of local intensities of the CT images. Intensive experiments have been carried out on two publicly available lung CT image data sets thereby achieving promising segmentation outcome and diagnosis efficiency (F1-score and AUC) while compared with the state of the art pre-trained convolutional based models.

show abstract

Section: Resultsmentioning

confidence: 94%

Auto-diagnosis of COVID-19 using Lung CT Images with Semi-supervised Shallow Learning Network

Konar

Panigrahi

Bhattacharyya

et al. 2020

Preprint

Self Cite

View full text Add to dashboard Cite

show abstract

“…The quantum version of the classical self-supervised neural network architectures [29]- [32] offer a potential solution for faster and efficient image segmentation and surpasses the classical counterparts. Konar et al recently developed quantum-inspired neural network models referred to as QIS-Net [4] and QIBDS-Net [5] suitable for brain MR image segmentation. These networks have been found to attain promising outcome in complete brain tumor segmentation.…”

Section: Related Workmentioning

confidence: 99%

“…The generalized form of V ox − QSig is obtained by leveraging the activation function hyper-parameters employed in Equation 19 as owing to the wide variations of gray-levels. Inspired by the authors' previous works [4], [22], [35], [36], the proposed Vox-QSig activation function employs four different adaptive thresholding schemes suitable for efficient gray-scale segmentation in the 3D-QNet architecture. In addition, to investigate a number of optimal thresholds [37] is explored.…”

Section: A Quantum-inspired Self-supervised Tensor Network Modelmentioning

confidence: 99%

See 1 more Smart Citation

3D Quantum-inspired Self-supervised Tensor Network for Volumetric Segmentation of Brain MR Images

Konar¹,

Bhattacharyya²,

Gandhi³

et al. 2020

Preprint

Self Cite

View full text Add to dashboard Cite

<div>This paper introduces a novel shallow self-supervised tensor neural network for volumetric segmentation of brain MR images obviating training or supervision. The proposed network is a 3D version of the Quantum-Inspired Self Supervised Neural Network (QIS-Net) architecture and is referred to as 3D Quantum-inspired Self-supervised Tensor Neural Network (3D-QNet). The underlying architecture of 3D-QNet is composed of a trinity of volumetric layers viz. input, intermediate and output layers inter-connected using a 26-connected third-order neighborhood-based topology for voxel-wise processing of 3D MR image data suitable for semantic segmentation. Each of the volumetric layers contains quantum neurons designated by qubits or quantum bits. The incorporation</div><div>of tensor decomposition in quantum formalism leads to faster convergence of the network operations to preclude the inherent slow convergence problems faced by the self-supervised networks. The segmented volumes are obtained once the network converges. The suggested 3D-QNet is tailored and tested on the BRATS 2019 data set extensively in the experiments carried out. 3D-QNet has achieved promising dice similarity while compared with the intensively supervised convolutional network-based models 3D-UNet, Vox-ResNet, DRINet, and 3D-ESPNet, thus facilitating annotation free semantic segmentation using a self-supervised shallow network.</div>

show abstract

“…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%

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

Konar¹,

Bhattacharyya²,

Panigrahi³

2020

Preprint

Self Cite

View full text Add to dashboard Cite

<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>

show abstract

A Quantum-Inspired Self-Supervised Network model for automatic segmentation of brain MR images

Cited by 34 publications

References 25 publications

Auto-diagnosis of COVID-19 using Lung CT Images with Semi-supervised Shallow Learning Network

Auto-diagnosis of COVID-19 using Lung CT Images with Semi-supervised Shallow Learning Network

3D Quantum-inspired Self-supervised Tensor Network for Volumetric Segmentation of Brain MR Images

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

Contact Info

Product

Resources

About