针对医学影像罕见病识别难题，动态调整分类边界提升准确率
DMDSC: A Dynamic-Margin Deep Simplex Classifier for Open-Set Recognition on Medical Image Datasets
- 根据病理频率自动调节类别间距离，稀有病种设更严边界
- 在四个医学数据集上显著优于当前最佳方法，尤其提升罕见病识别率
- 适合医疗影像中类别不平衡场景下的开放集识别任务
医学影像数据集常存在极端类别不平衡问题，罕见病灶远少于常见病。这给开放集识别（OSR）带来双重挑战：既要保持已知类别的高分类准确率，又需在临床中可靠拒绝训练时未见的未知样本。尽管近期提出的深度单纯形分类器（DSC）和不确定性感知深度单纯形分类器（UCDSC）利用神经坍缩实现类间最大分离，但其采用统一边距，未考虑医学类别密度差异。本文提出动态边距深度单纯形分类器（DMDSC），通过自动根据标签频率调整类别专属边距，对罕见病灶施加更高惩罚并强化特征聚类，以缓解数据不平衡影响。在BloodMNIST、OCTMNIST、DermaMNIST和BreaKHis等多个医学基准上的实验表明，该框架显著优于现有先进方法。
原文摘要 · Abstract (English)
Medical imaging datasets are often characterized by extreme class imbalances, where rare pathologies are significantly underrepresented compared to common conditions. This imbalance poses a dual challenge for Open-Set Recognition (OSR): models must maintain high classification accuracy on known classes while reliably rejecting unknown samples unseen during training in the clinical settings. While recently proposed Deep Simplex Classifier (DSC)~\cite{cevikalp2024reaching} and UnCertainty-aware Deep Simplex Classifier (UCDSC)~\cite{Aditya_2026_WACV} successfully leverage Neural Collapse to ensure maximal inter-class separation, they rely on a uniform margin that does not account for the varying densities of medical classes. In this paper, we propose DMDSC an enhanced framework featuring a dynamic margin approach. Our approach automatically adapts class-specific margins based on label frequency, enforcing a higher penalty and tighter feature clustering for rare pathologies to counteract the effects of data imbalance. Extensive experiments conducted on diverse medical benchmarks on BloodMNIST\cite{medmnistv2}, OCTMNIST\cite{medmnistv2}, DermaMNIST\cite{medmnistv2}, and BreaKHis~\cite{spanhol2015dataset} datasets, demonstrate that our framework outperforms state-of-the-art methods.
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