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

TL;DR

This work tackles pediatric TSC MRI classification by proposing QResNet, a hybrid model that fuses a 3D CNN backbone with a two-layer quantum layer to exploit quantum feature mappings. The quantum component combines a ZZFeatureMap encoding $U(\theta)=\prod_{k} U_k(\theta_k)$ with a RealAmplitudes Ansatz $V(\theta)$, enabling learning of richer representations for binary classification. Evaluated on 520 pediatric subjects with FLAIR and T2-weighted MRI, QResNet outperforms a 3D-ResNet34 baseline, achieving AUCs of $0.995$ (FLAIR) and $0.984$ (T2W) with high accuracies, demonstrating the feasibility of quantum-augmented imaging on realistic, small datasets. These results highlight the potential of quantum computing to enhance medical imaging diagnostics and motivate further work on scalable, real-world deployment of quantum algorithms in clinical settings.

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.

Figures (3)

• Figure 1: The Layers Structure of The Proposed QResNet
• Figure 2: Schematic of the data preprocessing pipeline
• Figure 3: The ROC curves of the proposed QResNet