Pediatric TSC-Related Epilepsy Classification from Clinical MR Images Using Quantum Neural Network

• URL: http://arxiv.org/abs/2408.12615v2
• Date: Mon, 26 Aug 2024 14:06:59 GMT
• Title: Pediatric TSC-Related Epilepsy Classification from Clinical MR Images Using Quantum Neural Network
• Authors: Ling Lin, Yihang Zhou, Zhanqi Hu, Dian Jiang, Congcong Liu, Shuo Zhou, Yanjie Zhu, Jianxiang Liao, Dong Liang, Hairong Zheng, Haifeng Wang,
• Abstract summary: This study introduces QResNet, a novel deep learning model seamlessly integrating conventional convolutional neural networks with quantum neural networks. A comprehensive evaluation, demonstrates the superior performance of QResNet in TSC MRI image classification compared to conventional 3D-ResNet models.
• Score: 17.788579893962492
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Tuberous sclerosis complex (TSC) manifests as a multisystem disorder with significant neurological implications. This study addresses the critical need for robust classification models tailored to TSC in pediatric patients, introducing QResNet,a novel deep learning model seamlessly integrating conventional convolutional neural networks with quantum neural networks. The model incorporates a two-layer quantum layer (QL), comprising ZZFeatureMap and Ansatz layers, strategically designed for processing classical data within a quantum framework. A comprehensive evaluation, demonstrates the superior performance of QResNet in TSC MRI image classification compared to conventional 3D-ResNet models. These compelling findings underscore the potential of quantum computing to revolutionize medical imaging and diagnostics.Remarkably, this method surpasses conventional CNNs in accuracy and Area Under the Curve (AUC) metrics with the current dataset. Future research endeavors may focus on exploring the scalability and practical implementation of quantum algorithms in real-world medical imaging scenarios.

Related papers

• An ensemble framework approach of hybrid Quantum convolutional neural networks for classification of breast cancer images [2.1659912179830023]
  Quantum neural networks are deemed suitable to replace classical neural networks in their ability to learn and scale up network models. Medical image classification pertains well to applications in deep learning, particularly, convolutional neural networks.
  arXiv  Detail & Related papers  (2024-09-24T10:43:27Z)
• Contrastive Learning in Memristor-based Neuromorphic Systems [55.11642177631929]
  Spiking neural networks have become an important family of neuron-based models that sidestep many of the key limitations facing modern-day backpropagation-trained deep networks. In this work, we design and investigate a proof-of-concept instantiation of contrastive-signal-dependent plasticity (CSDP), a neuromorphic form of forward-forward-based, backpropagation-free learning.
  arXiv  Detail & Related papers  (2024-09-17T04:48:45Z)
• CompressedMediQ: Hybrid Quantum Machine Learning Pipeline for High-Dimensional Neuroimaging Data [1.3359321655273804]
  This paper introduces CompressedMediQ, a novel hybrid quantum-classical machine learning pipeline. It addresses the computational challenges associated with high-dimensional multi-class neuroimaging data analysis.
  arXiv  Detail & Related papers  (2024-09-13T07:03:01Z)
• Parallel Proportional Fusion of Spiking Quantum Neural Network for Optimizing Image Classification [10.069224006497162]
  We introduce a novel architecture termed Parallel Proportional Fusion of Quantum and Spiking Neural Networks (PPF-QSNN) The proposed PPF-QSNN outperforms both the existing spiking neural network and the serial quantum neural network across metrics such as accuracy, loss, and robustness. This study lays the groundwork for the advancement and application of quantum advantage in artificial intelligent computations.
  arXiv  Detail & Related papers  (2024-04-01T10:35:35Z)
• ShadowNet for Data-Centric Quantum System Learning [188.683909185536]
  We propose a data-centric learning paradigm combining the strength of neural-network protocols and classical shadows. Capitalizing on the generalization power of neural networks, this paradigm can be trained offline and excel at predicting previously unseen systems. We present the instantiation of our paradigm in quantum state tomography and direct fidelity estimation tasks and conduct numerical analysis up to 60 qubits.
  arXiv  Detail & Related papers  (2023-08-22T09:11:53Z)
• K-Space-Aware Cross-Modality Score for Synthesized Neuroimage Quality Assessment [71.27193056354741]
  The problem of how to assess cross-modality medical image synthesis has been largely unexplored. We propose a new metric K-CROSS to spur progress on this challenging problem. K-CROSS uses a pre-trained multi-modality segmentation network to predict the lesion location.
  arXiv  Detail & Related papers  (2023-07-10T01:26:48Z)
• A Hybrid Neural Coding Approach for Pattern Recognition with Spiking Neural Networks [53.31941519245432]
  Brain-inspired spiking neural networks (SNNs) have demonstrated promising capabilities in solving pattern recognition tasks. These SNNs are grounded on homogeneous neurons that utilize a uniform neural coding for information representation. In this study, we argue that SNN architectures should be holistically designed to incorporate heterogeneous coding schemes.
  arXiv  Detail & Related papers  (2023-05-26T02:52:12Z)
• Problem-Dependent Power of Quantum Neural Networks on Multi-Class Classification [83.20479832949069]
  Quantum neural networks (QNNs) have become an important tool for understanding the physical world, but their advantages and limitations are not fully understood. Here we investigate the problem-dependent power of QCs on multi-class classification tasks. Our work sheds light on the problem-dependent power of QNNs and offers a practical tool for evaluating their potential merit.
  arXiv  Detail & Related papers  (2022-12-29T10:46:40Z)
• Quantum-classical convolutional neural networks in radiological image classification [0.0]
  Some quantum machine learning algorithms might result in improved training capabilities with respect to their classical counterparts. Within this paper, different hybrid quantum-classical convolutional neural networks (QCCNN) with varying quantum circuit designs and encoding techniques are proposed.
  arXiv  Detail & Related papers  (2022-04-26T15:47:19Z)
• On Circuit-based Hybrid Quantum Neural Networks for Remote Sensing Imagery Classification [88.31717434938338]
  The hybrid QCNNs enrich the classical architecture of CNNs by introducing a quantum layer within a standard neural network. The novel QCNN proposed in this work is applied to the Land Use and Land Cover (LULC) classification, chosen as an Earth Observation (EO) use case. The results of the multiclass classification prove the effectiveness of the presented approach, by demonstrating that the QCNN performances are higher than the classical counterparts.
  arXiv  Detail & Related papers  (2021-09-20T12:41:50Z)
• Qutrit-inspired Fully Self-supervised Shallow Quantum Learning Network for Brain Tumor Segmentation [7.173859338960338]
  Qubits or bi-level quantum bits often describe quantum neural network models. In this article, a novel self-supervised shallow learning network model is presented for automated segmentation of brain MR images. Results shed promising segmented outcome in detecting tumors in terms of dice similarity and accuracy with minimum human intervention and computational resources.
  arXiv  Detail & Related papers  (2020-09-14T22:15:22Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.