发表机构
Boise State University; Indiana University Indianapolis; University of Notre Dame; University of Cincinnati（博伊西州立大学; 印第安纳大学印第安纳波利斯分校; 圣母大学; 辛辛那提大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
针对小规模医学图像分类中噪声标签学习难题，提出基于上鞅的标签转换（SLT）框架用于鲁棒量子神经网络分类，通过建模熵减少为上鞅识别稳定转换步骤，实验证明其能提升分类性能并优于经典基线。
AI 中文摘要
小规模医学图像分类中的噪声标签学习具有挑战性，阻碍了深度神经网络的优势。近期研究表明量子神经网络（QNNs）在有限数据情况下有潜力，但用于噪声标签学习的探索仍不足。关键障碍是QNNs固有的“自然平滑性”。我们提出基于上鞅的标签转换（SLT），一种无锚点损失校正框架，用于有噪声标签下基于QNN的鲁棒医学图像分类。SLT将预测分布中的熵减少建模为上鞅，利用其单调行为识别稳定的转换矩阵细化步骤，减少训练振荡。实验表明SLT持续改进基于QNN的分类，优于经典噪声标签学习基线。
英文摘要
Noisy-label learning in small-scale medical image classification is challenging and hinders the superiority of deep neural networks. Recent studies suggest that quantum neural networks (QNNs) have shown potential in limited-data regimes, yet their use for noisy-label learning remains under-explored. A key obstacle is QNNs' intrinsic "natural smoothness", which may regularize training but also obscure high-confidence samples needed for noise-transition estimation. We propose Supermartingale-based Label Transition (SLT), an anchor-free loss correction framework for robust QNN-based medical image classification under noisy labels. SLT models entropy reduction in predictive distributions as a supermartingale and uses its monotonic behavior to identify stable transition-matrix refinement steps. This enables dynamic transition updates while reducing noise-driven oscillations during QNN training. We further provide a convergence analysis showing that the proposed transition-refinement process reaches a steady state. Experiments on multiple public small-scale medical image datasets demonstrate that SLT consistently improves QNN-based classification and stably outperforms classic noise-label learning baselines under synthetic and real-world label noise.
CommentsPreprint, under review
