Quantum-infused deep learning for dual-task thyroid ultrasound diagnostics
TL;DR
This study introduces a hybrid quantum– classical deep learning framework that leverages quantum variational filtering in tandem with transfer learning via ViT-B16 and EfficientNet-B3 to enhance feature representation and demonstrates promising robustness and generalization in limited-data environments.
Abstract
Accurate classification of thyroid nodules is essential for early intervention and improved clinical outcomes; however, its diagnostic performance remains limited by inter-observer variability, limited data availability, and class imbalance in ultrasound imaging. Although existing deep learning models have demonstrated strong performance, they often struggle to capture both global semantic context and fine-grained structural details under limited data. This study introduces a hybrid quantum– classical deep learning framework that leverages quantum variational filtering in tandem with transfer learning via ViT-B16 and EfficientNet-B3 to enhance feature representation. A multi-stage preprocessing pipeline was designed to improve input diversity and class balance. The model jointly performs binary tumor classification and Thyroid Imaging Reporting and Data System (TI-RADS) level prediction, achieving 94.2% accuracy for benign-versus-malignant tumor classification and 91.8% macro-accuracy for TI-RADS prediction. The proposed approach demonstrates promising robustness and generalization in limited-data environments, highlighting the potential of integrating quantum encoding with classical deep learning architectures for medical image diagnostics.