Abstract
Combining open-source datasets can introduce data leakage if the same subject appears in multiple sets, leading to inflated model performance. To address this, we explore subject fingerprinting, mapping all images of a subject to a distinct region in latent space, to enable subject re-identification via similarity matching. Using a ResNet-50 trained with triplet margin loss, we evaluate few-shot fingerprinting on 3D MRI and 2D X-ray data in both standard (20-way 1-shot) and challenging (1000-way 1-shot) scenarios. The model achieves high Mean- Recall-@-K scores: 99.10% (20-way 1-shot) and 90.06% (500-way 5-shot) on ChestXray-14; 99.20% (20-way 1-shot) and 98.86% (100-way 3-shot) on BraTS- 2021.
Abstract (translated)
将开源数据集合并时，如果同一个主体在多个数据集中出现，可能会引入数据泄漏问题，从而导致模型性能被高估。为了解决这个问题，我们探索了主体指纹识别技术，即把一个主题的所有图像映射到潜在空间中的独特区域中，以便通过相似性匹配来重新识别该主题。利用带有三元组边际损失的ResNet-50模型，在标准（20类1样本）和挑战性（1000类1样本）场景下评估了在3D MRI和2D X射线数据上的少量示例指纹识别性能。 模型获得了高Mean Recall @ K分数：在ChestXray-14上，分别为99.10%（20类1样本）和90.06%（500类5样本）；在BraTS 2021数据集上，分别为99.20%（20类1样本）和98.86%（100类3样本）。
URL
https://arxiv.org/abs/2512.16685
