Few-Shot Fingerprinting Subject Re-Identification in 3D-MRI and 2D-X-Ray
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Abstract (arXiv)
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.
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References · 10
- 2015Deep Residual Learning for Image Recognitioncited by 52,096
- 2017Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networkscited by 4,897
- 2015FaceNet: A Unified Embedding for Face Recognition and Clusteringcited by 2,849
- 2017ChestX-ray8: Hospital-scale Chest X-ray Database and Benchmarks on Weakly-Supervised Classification and Localization of Common Thorax Diseasescited by 972
- 2021The RSNA-ASNR-MICCAI BraTS 2021 Benchmark on Brain Tumor Segmentation and Radiogenomic Classificationcited by 518
- 2017Learning Spatio-Temporal Features with 3D Residual Networks for Action Recognitioncited by 178
- 2019Cross-Batch Memory for Embedding Learningcited by 132
- 2020Understanding SSIMcited by 77
- 2021Deep Learning-based Patient Re-identification Is able to Exploit the Biometric Nature of Medical Chest X-ray Datacited by 38
- 2023Benchmarking Pretrained Vision Embeddings for Near- and Duplicate Detection in Medical Imagescited by 3
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