Quantum integration in swin transformer mitigates overfitting in breast cancer screening.
Authors
Affiliations (8)
Affiliations (8)
- Department of Radiology, The First Affiliated Hospital of Bengbu Medical University, Bengbu, 233004, China.
- State Key Laboratory of Digital Medical Engineering, School of Biological Science & Medical Engineering, Southeast University, Nanjing, 210096, China.
- Hefei Benyuan Quantum Computing and Data Medicine Institute, Hefei, 230088, China.
- Origin Quantum Computing Technology (Hefei) Co., Ltd., Hefei, 230088, China.
- Department of Radiology, Zhongshan Hospital, Fudan University, Shanghai, 200030, China.
- Jiangsu Key Laboratory of Intelligent Medical Image Computing, School of Artificial Intelligence, Nanjing University of Information Science and Technology, Nanjing, 210044, China.
- Origin Quantum Computing Technology (Hefei) Co., Ltd., Hefei, 230088, China. [email protected].
- Hefei Benyuan Quantum Computing and Data Medicine Institute, Hefei, 230088, China. [email protected].
Abstract
To explore the potential of quantum computing in advancing transformer-based deep learning models for breast cancer screening, this study introduces the Quantum-Enhanced Swin Transformer (QEST). This model integrates a Variational Quantum Circuit (VQC) to replace the fully connected layer responsible for classification in the Swin Transformer architecture. In simulations, QEST exhibited competitive accuracy and generalization performance compared to the original Swin Transformer, while also demonstrating an effect in mitigating overfitting. Specifically, in 16-qubit simulations, the VQC reduced the parameter count by 62.5% compared with the replaced fully connected layer and improved the Balanced Accuracy (BACC) by 3.62% in external validation. Furthermore, validation experiments conducted on an actual quantum computer have corroborated the effectiveness of QEST.