Exemplar Diffusion: Improving Medical Object Detection with Opportunistic Labels

• URL: http://arxiv.org/abs/2603.15267v1
• Date: Mon, 16 Mar 2026 13:36:23 GMT
• Title: Exemplar Diffusion: Improving Medical Object Detection with Opportunistic Labels
• Abstract summary: We present a framework to take advantage of existing labels at inference, called textitexemplars, in order to improve the performance of object detection in medical images.<n>The method, textitexemplar diffusion, leverages existing diffusion methods for object detection to enable a training-free approach to adding information of known bounding boxes at test time.
• Score: 0.0
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: We present a framework to take advantage of existing labels at inference, called \textit{exemplars}, in order to improve the performance of object detection in medical images. The method, \textit{exemplar diffusion}, leverages existing diffusion methods for object detection to enable a training-free approach to adding information of known bounding boxes at test time. We demonstrate that for medical image datasets with clear spatial structure, the method yields an across-the-board increase in average precision and recall, and a robustness to exemplar quality, enabling non-expert annotation. Moreover, we demonstrate how our method may also be used to quantify predictive uncertainty in diffusion detection methods. Source code and data splits openly available online: https://github.com/waahlstrand/ExemplarDiffusion

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