提出新模型提升医疗图像未知类别识别能力，应对数据稀缺挑战。
UCDSC: Open Set UnCertainty aware Deep Simplex Classifier for Medical Image Datasets
- 基于深度网络特征的正则单纯形结构设计新型损失函数
- 在四个MedMNIST及皮肤数据集上超越现有最佳方法
- 适合处理罕见病等小样本或未知类场景的医学诊断
得益于深度学习的发展，计算机辅助诊断已取得显著进展。但在实际医疗环境中，算法常面临数据受限问题，尤其在新兴或罕见疾病中，因伦理与法律限制以及专家标注成本高昂导致数据不足。在此背景下，开放集识别至关重要：判断样本是否属于训练时见过的已知类别，或应被判定为未知。近期研究发现，深度神经网络后层特征会聚集在各自类别均值周围，这些均值构成规则单纯形的顶点。本文提出一种新损失函数，通过辅助数据集惩罚开放空间区域，有效识别未知类别。该方法在四个MedMNIST数据集（BloodMNIST、OCTMNIST、DermaMNIST、TissueMNIST）及一个公开皮肤数据集上表现优异，显著优于当前主流技术。
原文摘要 · Abstract (English)
Driven by advancements in deep learning, computer-aided diagnoses have made remarkable progress. However, outside controlled laboratory settings, algorithms may encounter several challenges. In the medical domain, these difficulties often stem from limited data availability due to ethical and legal restrictions, as well as the high cost and time required for expert annotations-especially in the face of emerging or rare diseases. In this context, open-set recognition plays a vital role by identifying whether a sample belongs to one of the known classes seen during training or should be rejected as an unknown. Recent studies have shown that features learned in the later stages of deep neural networks are observed to cluster around their class means, which themselves are arranged as individual vertices of a regular simplex [32]. The proposed method introduces a loss function designed to reject samples of unknown classes effectively by penalizing open space regions using auxiliary datasets. This approach achieves significant performance gain across four MedMNIST datasets-BloodMNIST, OCTMNIST, DermaMNIST, TissueMNIST and a publicly available skin dataset [29] outperforming state-of-the-art techniques.
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