在swin变压器中的量子集成减轻了乳腺癌查中的过度匹配
Zongyu Xie1,2,3, Xiaoguang Yang4,3, Shuni Zhang1,3
1Department of Radiology, The First Affiliated Hospital of Bengbu Medical University, Bengbu, 233004, China.
Scientific reports
|August 27, 2025
概括
量子计算可以提高乳腺癌查的深度学习能力. 量子增强旋转变压器 (QEST) 显示了改进的精度和降低的参数,在真实量子硬件上得到验证.
科学领域:
- 量子计算
- 人工智能
- 医学成像
背景情况:
- 转换器模型是医学图像分析的最先进的深度学习工具.
- 乳腺癌查的准确性对于早期发现和改善患者的结果至关重要.
- 整合量子计算可能为复杂的人工智能任务提供新的计算优势.
研究的目的:
- 研究量子计算在增强基于变压器的深度学习模型对乳腺癌查的有效性.
- 介绍和评估量子增强旋转变压器 (QEST) 模型.
- 评估QEST的性能与传统的Swin变压器模型相比.
主要方法:
- 该研究通过将变量量子电路 (VQC) 纳入Swin变压器架构来开发量子增强的Swin变压器 (QEST).
- VQC取代了Swin变压器的完全连接的分类层.
- 通过对实际量子计算机进行模拟和验证实验来评估性能.
主要成果:
- 与标准的Swin变压器相比,QEST表现出了竞争力的准确性和通用性.
- VQC 集成导致参数数量显著减少 (16 个量子比特模拟中的 62.5%).
- 在外部验证中,QEST提高了3.62%的平衡精度 (BACC),并显示了减轻过拟合的效果.
结论:
- 量子计算在乳腺癌查等医疗应用中具有显著的潜力.
- QEST模型提供了一个有前途的混合量子-经典方法,在保持或提高性能的同时降低了计算复杂性.
- 在实际量子硬件上的验证证实了QEST对乳腺癌查的实际可行性和有效性.
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