Exemplar Diffusion: Improving Medical Object Detection with Opportunistic Labels
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arXiv
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| Format: | Preprint |
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2026
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| _version_ | 1866910054686392320 |
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| author | Wåhlstrand, Victor Alvén, Jennifer Häggström, Ida |
| author_facet | Wåhlstrand, Victor Alvén, Jennifer Häggström, Ida |
| contents | 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 |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2603_15267 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Exemplar Diffusion: Improving Medical Object Detection with Opportunistic Labels Wåhlstrand, Victor Alvén, Jennifer Häggström, Ida Computer Vision and Pattern Recognition 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 |
| title | Exemplar Diffusion: Improving Medical Object Detection with Opportunistic Labels |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2603.15267 |