利用测试时已知标注框提升医学图像目标检测性能
Exemplar Diffusion: Improving Medical Object Detection with Opportunistic Labels
- 通过扩散模型在推理时引入已知边界框信息
- 平均精度与召回率全面提升，对标注质量不敏感
- 可评估扩散检测方法的预测不确定性，适合非专家使用
我们提出一种框架，利用推理阶段已有的标签（称为'示例框'）来提升医学图像中目标检测的性能。该方法名为'示例扩散'，借助现有扩散模型实现无需训练的测试时信息增强，可动态融入已知边界框。在具有清晰空间结构的医学图像数据集上，该方法显著提升平均精度和召回率，且对示例框质量具有鲁棒性，适用于非专业标注。此外，该方法还可用于量化扩散检测模型的预测不确定性。源代码与数据划分已公开：https://github.com/waahlstrand/ExemplarDiffusion
原文摘要 · Abstract (English)
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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