# Exemplar Diffusion: Improving Medical Object Detection with Opportunistic Labels

arXiv: https://arxiv.org/abs/2603.15267 · HTML: https://mlsanity.com/paper/2603.15267/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 19 of its 27 references that are in the map.
- First posted on arXiv 16 Mar 2026.

## Builds on (most cited first, 19 of 19)

- [Denoising Diffusion Probabilistic Models](https://mlsanity.com/paper/2006.11239/) (2020)
- [Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks](https://mlsanity.com/paper/1506.01497/) (2015)
- [Segment Anything](https://mlsanity.com/paper/2304.02643/) (2023)
- [End-to-End Object Detection with Transformers](https://mlsanity.com/paper/2005.12872/) (2020)
- [SAM 3: Segment Anything with Concepts](https://mlsanity.com/paper/2511.16719/) (2025)
- [Sparse R-CNN: End-to-End Object Detection with Learnable Proposals](https://mlsanity.com/paper/2011.12450/) (2020)
- [DiffusionDet: Diffusion Model for Object Detection](https://mlsanity.com/paper/2211.09788/) (2022)
- [VerSe: A Vertebrae Labelling and Segmentation Benchmark for Multi-detector CT Images](https://mlsanity.com/paper/2001.09193/) (2020)
- [Prior-aware Neural Network for Partially-Supervised Multi-Organ Segmentation](https://mlsanity.com/paper/1904.06346/) (2019)
- [Learning from Multiple Datasets with Heterogeneous and Partial Labels for Universal Lesion Detection in CT](https://mlsanity.com/paper/2009.02577/) (2020)
- [Estimating and Evaluating Regression Predictive Uncertainty in Deep Object Detectors](https://mlsanity.com/paper/2101.05036/) (2021)
- [Deep Learning under Privileged Information Using Heteroscedastic Dropout](https://mlsanity.com/paper/1805.11614/) (2018)
- [Diffusion-Based Hierarchical Multi-Label Object Detection to Analyze Panoramic Dental X-rays](https://mlsanity.com/paper/2303.06500/) (2023)
- [CADS: A Comprehensive Anatomical Dataset and Segmentation for Whole-Body Anatomy in Computed Tomography](https://mlsanity.com/paper/2507.22953/) (2025)
- [DiffULD: Diffusive Universal Lesion Detection](https://mlsanity.com/paper/2303.15728/) (2023)
- [A Curated and Re-annotated Peripheral Blood Cell Dataset Integrating Four Public Resources](https://mlsanity.com/paper/2407.13214/) (2024)
- [Explainable vertebral fracture analysis with uncertainty estimation using differentiable rule-based classification](https://mlsanity.com/paper/2407.02926/) (2024)
- [Using Ensemble Diffusion to Estimate Uncertainty for End-to-End Autonomous Driving](https://mlsanity.com/paper/2506.00560/) (2025)
- [TSBP: Improving Object Detection in Histology Images via Test-time Self-guided Bounding-box Propagation](https://mlsanity.com/paper/2409.16678/) (2024)

## Abstract (arXiv)

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