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International Journal of Computer Assisted Radiology and Surgery

Vision transformer embeddings and quantum pyramidal circuits for biomedical image analysis

Xavier Font-Aragonès, Miguel Á. González Ballester

Published May 25, 2026• 0 citations
Publisher

Abstract

Abstract This work presents a novel quantum-hybrid pipeline for lung nodule classification in computed tomography (CT) scans, combining vision transformer (ViT) embeddings with quantum orthogonal pyramidal circuits (QOPCs). The approach was evaluated on 681 lung nodule CT scans across axial, coronal, and sagittal planes. Two ViT configurations were tested: ViT 1 (1 head, 4 layers) and a Bayesian-optimized ViT 2 (4 heads, 8 layers). Features extracted from ViT embedding layers were reduced via principal component analysis to 2–16 dimensions and classified using the QOPC with reconfigurable beam splitter (RBS) gates. The proposed approach achieved unprecedented compression, up to 1,470 $$\times $$ × (from 10,290 to 7 parameters) while preserving over 99% of baseline accuracy. The approach reached 83.7% accuracy (ViT 2 , $$k{=}8$$ k = 8 , $$h{=}8$$ h = 8 ) with only 46 trainable parameters and achieved computational efficiency (CE) up to 92.0. Training was accelerated up to 28 $$\times $$ × (0.030 vs. 0.833 min) while maintaining robust diagnostic performance (F1: 0.77−0.82, receiver operating characteristic area under the curve (ROC-AUC): 0.87−0.90). Ablation studies confirmed that the quantum layer outperforms conventional MLPs by +3.4% accuracy with 35% fewer parameters, while late fusion of multi-view predictions further improved performance to 85.4% accuracy and 0.92 ROC-AUC. These results establish hybrid ViT-QOPC architectures as a practical and resource-efficient framework for medical image analysis, demonstrating their ability to dramatically reduce computational cost without compromising clinical accuracy.

DOI
10.1007/s11548-026-03696-y

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Abstract This work presents a novel quantum-hybrid pipeline for lung nodule classification in computed tomography (CT) scans, combining vision transformer (ViT) embeddings with quantum orthogonal pyramidal circuits (QOPCs). The approach was evaluated on 681 lung nodule CT scans across axial, coronal, and sagittal planes. Two ViT configurations were tested: ViT 1 (1 head, 4 layers) and a Bayesian-optimized ViT 2 (4 heads, 8 layers). Features extracted from ViT embedding layers were reduced via principal component analysis to 2–16 dimensions and classified using the QOPC with reconfigurable beam splitter (RBS) gates. The proposed approach achieved unprecedented compression, up to 1,470 ×\times × × (from 10,290 to 7 parameters) while preserving over 99% of baseline accuracy. The approach reached 83.7% accuracy (ViT 2 , k=8k{=}8k=8 k = 8 , h=8h{=}8h=8 h = 8 ) with only 46 trainable parameters and achieved computational efficiency (CE) up to 92.0. Training was accelerated up to 28 ×\times × × (0.030 vs. 0.833 min) while maintaining robust diagnostic performance (F1: 0.77−0.82, receiver operating characteristic area under the curve (ROC-AUC): 0.87−0.90). Ablation studies confirmed that the quantum layer outperforms conventional MLPs by +3.4% accuracy with 35% fewer parameters, while late fusion of multi-view predictions further improved performance to 85.4% accuracy and 0.92 ROC-AUC. These results establish hybrid ViT-QOPC architectures as a practical and resource-efficient framework for medical image analysis, demonstrating their ability to dramatically reduce computational cost without compromising clinical accuracy.
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Abstract Pulmonary nodule classification remains constrained by variability in computed tomography (CT) acquisition protocols, annotation quality, and the extreme class imbalance that hinders accurate decision–making in diagnosis. Traditional deep learning approaches have been severely limited by the lack of diversity in training datasets, their tendency to be overly sensitive to noise, and their inability to account for the large number of nonlinear feature interactions present in volumetric structures. Therefore, a dual–phase framework is presented to investigate the application of classical and hybrid quantum–classical (HQC) architectures. Phase 1 evaluates CNN and hybrid Quantum–classical (HQC) feature extractors to quantify quantum expressivity. The HQC model employs a series of layered variational quantum circuits (VQCs) with calibrated rotation gates and structured entanglement patterns to expand the hypothesis space while improving operational stability. HQC achieved superior ROC–AUC (0.89 ± 0.02) and recall (0.86 ± 0.03). Phase 2 benchmarks the performance of Swin–Transformer embeddings with classical classifiers, as well as the HQC for a fair comparison. The HQC constantly outperforms other methods (ROC–AUC = 0.73 ± 0.02), indicating robustly enhanced quantum representations. Conditional Generative Adversarial Networks (CGANs) were used to address the extreme dataset imbalance. This dissertation provides statistically valid evidence that HQC architectures significantly increase both classification sensitivity and stability, thereby offering an opportunity for future research on precision oncology under high-variance, data-constrained conditions.
Medical image classification has been a fundamental task in computer-aided diagnosis. In this work, we propose a hybrid quantum-classical deep learning framework that integrates a parameterized quantum layer into the ResNet-18 architecture for medical image classification. Our experiments are conducted on the OrganMNIST-Axial, OrganMNIST-Coronal, and OrganMNIST-Sagittal datasets from the MedMNIST v2 collection, each consisting of 28×2828 \times 2828×28 grayscale images representing different organ sections. The quantum layer is implemented as a 6-qubit variational quantum circuit (VQC) that partially replaces the classical fully connected module, enabling the network to exploit quantum feature mapping and entanglement for enhanced nonlinear representation learning. This architectural modification reduces the number of classical trainable parameters by approximately 44% compared with the original ResNet-18 while preserving its representational capacity. We evaluate the proposed QResNet18 against classical baselines to investigate its potential advantages in terms of accuracy, robustness, and parameter efficiency. Experimental results demonstrate that the hybrid quantum-classical model achieves competitive or superior performance while maintaining a significantly reduced parameter count, highlighting the feasibility and promise of quantumenhanced medical image classification.
Purpose This study investigates the integration of quantum-enhanced machine learning (QML) into medical image analysis, focusing on classification and feature extraction tasks. It aims to evaluate whether hybrid quantum-classical models can overcome the limitations of traditional machine learning in handling high-dimensional, clinically relevant imaging data. Design/methodology/approach Hybrid architectures combining classical preprocessing with quantum components, specifically variational quantum circuits (VQCs) and quantum kernel methods, were developed and implemented in simulated quantum environments using Qiskit and PennyLane on Google Colab. Publicly available datasets (ChestX-ray14, Brain Tumor MRI and CT Hemorrhage) were preprocessed and encoded into quantum states. Model performance was evaluated against classical baselines (MLP and CNN) using accuracy, precision, recall, F1-score, convergence speed and trainable parameters. Findings Quantum-enhanced models achieved competitive or superior performance compared to classical models with significantly fewer trainable parameters and faster convergence. The Quantum Kernel SVM demonstrated the highest precision (89.1%) and F1-score (88.5%) using only 8 parameters, while VQC models converged up to 2× faster than classical baselines. These results highlight the potential of QML to improve efficiency, robustness and scalability in diagnostic imaging workflows, particularly in resource-constrained or data-limited settings. Research limitations/implications The study relies on simulated quantum environments, which do not capture hardware noise or qubit limitations. Future work will extend to real quantum devices, multimodal data integration and interpretability strategies. Practical implications Quantum kernel methods and lightweight VQC architectures enable faster, resource-efficient diagnostic modeling, making them suitable for clinical environments with limited computational capacity or time-sensitive decision-making needs. Social implications Improving the efficiency and accessibility of medical image analysis has the potential to support faster diagnosis and enhance healthcare delivery, particularly in underserved or resource-limited regions. Originality/value This study provides a practical evaluation of hybrid quantum-classical models applied to multiple real medical imaging datasets in a reproducible simulated environment. It offers an early-stage framework demonstrating how QML techniques can be integrated into existing machine learning pipelines for medical image analysis.
Medical image classification is a critical component of modern healthcare; however, accurate diagnosis remains challenging due to limited annotated datasets, class imbalance, and the high dimensionality of medical imaging data. To address these challenges, a hybrid quantum-classical neural network (HQCNN) is proposed, integrating classical deep learning with variational quantum learning for medical image classification. The proposed architecture combines a five-layer convolutional neural network (CNN) for hierarchical feature extraction with a lightweight 4-qubit variational quantum circuit (VQC) incorporating quantum state encoding, superposition and entanglement mechanisms, and a quantum attention-Fourier (QAF) module. This hybrid design aims to improve nonlinear feature representation and quantum parameter efficiency while maintaining a shallow quantum circuit suitable for noisy intermediate-scale quantum (NISQ)-era constraints. Experimental evaluation on six MedMNIST benchmark datasets demonstrated competitive performance across both binary and multi-class classification tasks. HQCNN achieved 98.88% accuracy on the binary subset of PathMNIST (classes 0 vs. 1), 97.61% accuracy on the multi-class OrganAMNIST dataset, and 86.29% accuracy on BreastMNIST. Comparative experiments and statistical analyses demonstrated consistent improvements over the controlled BHQNN baseline, while component-wise ablation studies showed that the QAF module, superposition and entanglement mechanisms, and expressive parameterized rotations contributed to classification performance in a complementary and dataset-dependent manner. Moreover, HQCNN reduced the number of trainable quantum parameters by approximately 55.6% compared with the baseline hybrid quantum neural network (BHQNN). Noise-aware simulations further showed that the model retained relatively stable predictive performance under moderate depolarizing noise, supporting further evaluation under near-term quantum computing conditions. Overall, the results demonstrate that HQCNN provides a parameter-efficient hybrid quantum-classical framework for medical image classification and offers a promising foundation for further investigation of quantum-enhanced medical image analysis.
Although automated diagnosis in medical imaging has made tremendous breakthroughs with deep learning, current models also exhibit difficulties with representation stability, calibration, and robustness to domain heterogeneity. Traditional methods that use deeper CNN or transformer networks incur greater computational costs without fully addressing feature separability and uncertainty estimation. To address these shortcomings, the present study proposes a hybrid quantum-classical approach, VQMedNet, that integrates variational quantum circuits with classical feature extraction for supervised classification of MRI and CT images. The suggested QFeatEmbed-VQC module is an NISQ-conscious nonlinear quantum feature embedding that can be trained on tokenised and compressed image features, enabling effective learning with constrained quantum resources. A gradual training approach is followed to achieve consistent optimisation, and calibration-sensitive assessment is included to measure the predictability. The experiments with the brain tumour MRI (BraTS) and lung CT (LIDC-IDRI) datasets show a stable increase over the classical baseline, achieving 95.6% accuracy and 0.947 Macro-F1 on MRI classification, and 93.1% accuracy with improved AUC-PR on CT tasks. Additional analyses show enhanced embedding separability, robustness to domain perturbations, and reduced Expected Calibration Error. Moreover, the findings suggest that moderate-depth quantum circuits offer a good balance between performance and computational complexity. Although tested under simulation-based conditions, the proposed framework can indicate the potential of quantum-enhanced feature embedding for building stable and efficient medical AI systems. It may be seen as a promising approach to future NISQ-aware diagnostics. The implementation is publicly available at: https://github.com/gvvsrk/QuantumMed-FedDx . Clinical trial number: Not applicable.