Accurate localization of thoracic abnormalities in chest X-ray images remains a major challenge due to the limited
availability of large-scale, finely annotated datasets. Few-shot learning has recently emerged as a promising strategy to
address this problem by enabling models to generalize to unseen categories with only a small number of labeled
examples. In this work, we propose an improved few-shot localization approach for VinDr-CXR images by leveraging
the DINO-DETR model, a transformer-based detection framework with self-supervised pretraining. Our method
adapts DINO-DETR to the few-shot setting through task specific fine-tuning and optimization strategies designed to
improve feature alignment between support and query samples. Experimental results demonstrate
that the proposed method achieves competitive localization accuracy compared to baseline approaches, while reducing
the reliance on large annotated datasets. Although certain predictions remain imperfect, particularly in cases with subtle
or overlapping pathologies, the approach shows clear potential for scaling to broader medical imaging applications.
This study highlights both the opportunities and limitations of applying state-of-the-art transformer-based detection
architectures to few-shot medical image localization and suggests directions for future improvements, such as data
augmentation and cross-domain pretraining.