研究人员开发了一种名为DMDSC的新分类器，旨在改善在存在极端类别不平衡的医学成像数据集中进行开放集识别的性能。这种动态边距方法根据标签频率调整边距，对罕见病理施加更严格的惩罚和更紧密的特征聚类。在BloodMNIST和OCTMNIST等数据集上的实验表明，DMDSC的性能优于现有的最先进方法。 AI
影响 改善了对不平衡医学数据集的处理，以更好地检测罕见病理和拒绝未知样本。
排序理由 介绍一种用于医学成像的新分类方法的学术论文。
AI 生成摘要 · Google Gemini · 来自 2 个来源。 我们如何撰写摘要 →
arXiv:2605.00675v1 Announce Type: new Abstract: 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 (O…
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 ac…