Exemplar Diffusion: Improving Medical Object Detection with Opportunistic Labels

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. 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. 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