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This paper introduces a novel exemplar-based approach to robust abnormality detection in Chest X-Ray (CXR) images, significantly reducing the need for extensive annotations. By leveraging domain-aware contrastive optimization and exemplar feature generation, the method adapts effectively to new disease findings without requiring exhaustive retraining. The results show that this approach achieves near state-of-the-art performance with less than 10% of the annotated data, highlighting its potential for practical clinical applications.
Achieving near state-of-the-art CXR detection with under 10% of the usual annotations could revolutionize medical imaging workflows.
Reducing annotation requirements remains a key challenge in developing robust medical object detectors. To address this, Vision-Language (VL) object detection methods leverage grounding text information to enable powerful zero-shot and few-shot object detectors in the natural image domain [1, 2, 3, 4]. However, transferring these methods to the medical domain is challenging due to the absence of comparable quality and quantity of the grounding data. Regardless, significant contextual and non-imaging information exists in medical images that remains underutilized. Few-shot learning (FSL) techniques partially address this limitation but struggle to general ize to unseen medical findings and require extensive retraining when new findings are introduced [5, 6]. To overcome these challenges, we extend our prior EM-DETR framework [7] and introduce a scalable FS detection approach designed for efficient abnormality detection in Chest X-Ray (CXR) images under minimal supervision. The proposed architecture incorporates exemplar-based feature generation and domain-aware contrastive optimization, enabling effective adaptation to novel disease findings without exhaustive retraining. Our method achieves near state-of-the-art (SOTA) detection performance using less than 10% of the annotated data, demonstrating its potential for practical, annotation-efficient clinical deployment across both proprietary and public CXR datasets.
