{"slug": "slt-robust-quantum-neural-networks-for-noisy-label-medical-image-classification", "title": "SLT: Robust Quantum Neural Networks for Noisy-Label Medical Image Classification via Supermartingale-based Label Transition", "summary": "Researchers propose Supermartingale-based Label Transition (SLT), an anchor-free loss correction framework for robust quantum neural network (QNN)-based medical image classification under noisy labels. SLT models entropy reduction as a supermartingale to dynamically refine transition matrices, reducing noise-driven oscillations during QNN training. Experiments on small-scale medical image datasets show SLT consistently improves QNN classification and outperforms classic noisy-label learning baselines under synthetic and real-world label noise.", "body_md": "arXiv:2607.16293v1 Announce Type: new\nAbstract: 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.", "url": "https://wpnews.pro/news/slt-robust-quantum-neural-networks-for-noisy-label-medical-image-classification", "canonical_source": "https://arxiv.org/abs/2607.16293", "published_at": "2026-07-21 04:00:00+00:00", "updated_at": "2026-07-21 04:09:05.470434+00:00", "lang": "en", "topics": ["machine-learning", "neural-networks", "computer-vision"], "entities": [], "alternates": {"html": "https://wpnews.pro/news/slt-robust-quantum-neural-networks-for-noisy-label-medical-image-classification", "markdown": "https://wpnews.pro/news/slt-robust-quantum-neural-networks-for-noisy-label-medical-image-classification.md", "text": "https://wpnews.pro/news/slt-robust-quantum-neural-networks-for-noisy-label-medical-image-classification.txt", "jsonld": "https://wpnews.pro/news/slt-robust-quantum-neural-networks-for-noisy-label-medical-image-classification.jsonld"}}