Example-based Robust Abnormality Detection with Minimal Annotations using Exemplar Med-DETR

• URL: http://arxiv.org/abs/2608.24281v1
• Date: Tue, 25 Aug 2026 09:06:23 GMT
• Title: Example-based Robust Abnormality Detection with Minimal Annotations using Exemplar Med-DETR
• Abstract summary: We introduce a scalable FS detection approach designed for efficient abnormality detection in Chest X-Ray (CXR) images under minimal supervision.<n>Our method achieves near state-of-the-art (SOTA) detection performance using less than 10% of the annotated data.
• Score: 4.558123049022306
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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