Few-Shot Fingerprinting Subject Re-Identification in 3D-MRI and 2D-X-Ray
This addresses data leakage issues for researchers using medical imaging datasets, though it is incremental as it applies existing methods to new data.
The paper tackles the problem of data leakage in combined open-source datasets by developing a subject fingerprinting method for re-identification, achieving high Mean-Recall-@-K scores such as 99.10% in 20-way 1-shot and 90.06% in 500-way 5-shot scenarios on medical imaging data.
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.