A Quantum-Inspired Self-Supervised Network model for automatic segmentation of brain MR images
- Title
- A Quantum-Inspired Self-Supervised Network model for automatic segmentation of brain MR images
- Creator
- Konar D.; Bhattacharyya S.; Gandhi T.K.; Panigrahi B.K.
- Description
- The classical self-supervised neural network architectures suffer from slow convergence problem and incorporation of quantum computing in classical self-supervised networks is a potential solution towards it. In this article, a fully self-supervised novel quantum-inspired neural network model referred to as Quantum-Inspired Self-Supervised Network (QIS-Net) is proposed and tailored for fully automatic segmentation of brain MR images to obviate the challenges faced by deeply supervised Convolutional Neural Network (CNN) architectures. The proposed QIS-Net architecture is composed of three layers of quantum neuron (input, intermediate and output) expressed as qbits. The intermediate and output layers of the QIS-Net architecture are inter-linked through bi-directional propagation of quantum states, wherein the image pixel intensities (quantum bits) are self-organized in between these two layers without any external supervision or training. Quantum observation allows to obtain the true output once the superimposed quantum states interact with the external environment. The proposed self-supervised quantum-inspired network model has been tailored for and tested on Dynamic Susceptibility Contrast (DSC) brain MR images from Nature data sets for detecting complete tumor and reported promising accuracy and reasonable dice similarity scores in comparison with the unsupervised Fuzzy C-Means clustering, self-trained QIBDS Net, Opti-QIBDS Net, deeply supervised U-Net and Fully Convolutional Neural Networks (FCNNs). 2020 Elsevier B.V.
- Source
- Applied Soft Computing Journal, Vol-93
- Date
- 2020-01-01
- Publisher
- Elsevier Ltd
- Subject
- Fully Convolutional Neural Network; Medical image segmentation; QIBDS Net; Quantum computing; U-Net
- Coverage
- Konar D., Department of Electrical Engineering, Indian Institute of Technology Delhi, New Delhi, India, Department of Computer Science and Engineering, Sikkim Manipal Institute of Technology, Sikkim Manipal University, Sikkim, India; Bhattacharyya S., Department of Computer Science and Engineering, CHRIST (Deemed to be University), Bangalore, India; Gandhi T.K., Department of Electrical Engineering, Indian Institute of Technology Delhi, New Delhi, India; Panigrahi B.K., Department of Electrical Engineering, Indian Institute of Technology Delhi, New Delhi, India
- Rights
- Restricted Access
- Relation
- ISSN: 15684946
- Format
- Online
- Language
- English
- Type
- Article
Collection
Citation
Konar D.; Bhattacharyya S.; Gandhi T.K.; Panigrahi B.K., “A Quantum-Inspired Self-Supervised Network model for automatic segmentation of brain MR images,” CHRIST (Deemed To Be University) Institutional Repository, accessed February 27, 2025, https://archives.christuniversity.in/items/show/16281.