arXiv cs.AI· Yasmin Rodrigues Sobrinho, Jo\~ao Renato Ribeiro Manesco, Jo\~ao Paulo Papa·· 13 小时前AI 评分18
量子与经典特征互补：面向乳腺癌分类的自适应混合量子-经典特征融合
On the Complementarity of Quantum and Classical Features: Adaptive Hybrid Quantum-Classical Feature Fusion for Breast Cancer Classification
AI 导读
研究提出双分支混合量子-经典架构用于乳腺癌诊断，融合经典模型与可训练或确定性量子电路的特征，并给出静态融合（SHF）、动态融合（DHF）和温度缩放融合（TSHF）三种策略。
正文 · 原文
Abstract:The integration of quantum machine learning with classical deep learning offers promising avenues for medical image analysis by mapping data into high-dimensional Hilbert spaces. However, unifying these paradigms remains challenging due to optimization asymmetries. We propose a hybrid quantum-classical architecture for breast cancer diagnosis based on a dual-branch feature-extraction pipeline. Our framework unifies complementary representations from classical models and trainable or deterministic (non-trainable) quantum circuits. Three fusion strategies are introduced: Static Hybrid Fusion (SHF) for offline extraction, Dynamic Hybrid Fusion (DHF) for end-to-end co-adaptation, and Temperature-Scaled Hybrid Fusion (TSHF). Inspired by multimodal learning, TSHF uses a learnable scalar to balance branch gradient dynamics and address optimization bottlenecks. We evaluate BreastMNIST, INbreast, and BUS-UCLM, spanning ultrasound and mammography, using ResNet-18 and a shallow CNN, with five independent runs per configuration. Fusion improves over the strongest classical baseline on all three benchmarks. On BreastMNIST, TSHF with ResNet-18 and a non-trainable circuit achieves 88.33% accuracy, 92.09% F1-score, and 92.09% AUC-ROC, versus 84.74% accuracy and 87.08% AUC-ROC for ResNet-18 alone. On INbreast, SHF with the same pairing performs best, at 70.42% accuracy, 72.96% F1-score, and 80.28% AUC-ROC. On BUS-UCLM, SHF with a trainable circuit achieves 78.46% accuracy and 84.66% F1-score, over seven accuracy points above the best standalone branch. Adaptive fusion is not universally beneficial: static fusion performs best on the two smaller datasets, suggesting an additional estimation burden from co-adaptation and learned calibration under limited data. Clinical applicability requires validation beyond the small, low-resolution datasets and simulated quantum circuits considered here.
| Comments: | 41 pages, 16 figures. This manuscript is a preprint under review at Artificial Intelligence in Medicine |
| Subjects: | Computer Vision and Pattern Recognition (cs.CV); Artificial Intelligence (cs.AI) |
| Cite as: | arXiv:2604.22903 [cs.CV] |
| (or arXiv:2604.22903v2 [cs.CV] for this version) | |
| https://doi.org/10.48550/arXiv.2604.22903 arXiv-issued DOI via DataCite |
Submission history
From: João Renato Ribeiro Manesco [view email]
[v1]
Fri, 24 Apr 2026 14:54:53 UTC (5,183 KB)
[v2]
Tue, 20 Oct 2026 18:09:37 UTC (14,284 KB)
来源：arXiv cs.AI · arxiv.org