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.
@article{arxiv.2512.16685,
title = {Few-Shot Fingerprinting Subject Re-Identification in 3D-MRI and 2D-X-Ray},
author = {Gonçalo Gaspar Alves and Shekoufeh Gorgi Zadeh and Andreas Husch and Ben Bausch},
journal= {arXiv preprint arXiv:2512.16685},
year = {2025}
}