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

2026-08-25

AI Summary

该研究通过扩展EM-DETR框架，利用样本特征生成和领域感知对比优化方法，在最少标注下实现胸部X光图像中异常的有效检测。

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.

Problem

• annotation requirements
• medical object detectors
• minimal supervision
• Chest X-Ray (CXR) images
• few-shot learning

Innovation

• Exemplar-based feature generation
• Domain-aware contrastive optimization
• Few-shot learning
• Minimal supervision