做医学影像或逆问题研究的团队，可以用KLIP无监督地检测异常区域，无需额外标注数据，值得试试。
研究人员提出了一种基于KL散度的分布外检测指标KLIP，用于逆问题中检测图像是否来自训练分布。该方法无需校准数据或了解偏移分布，能检测整张图像或图像局部区域的分布偏移。实验表明，KLIP能检测出细微但语义重要的偏移，如健康肝脏CT与肿瘤CT的差异，且适用于多种扩散模型、数据集和逆问题。代码已开源。
KLIP: localized distribution shift detection via KL-divergence with diffusion priors in Inverse Problems
Diffusion models have shown promising performance as data-driven priors for computational imaging, as well as some capacity to detect out-of-distribution (OOD) images. However, existing approaches to OOD detection often require some knowledge of the shifted distribution, fail to detect subtle or localized distribution shifts, and operate on full images, rather than the indirect measurements available in inverse problems. We propose an OOD detection metric based on the Kullback-Leibler divergence between the diffusion prior and the posterior distribution, that (i) does not require any calibration data or knowledge of the shifted distribution, and (ii) can detect whole images as OOD as well as localize OOD patches within an image. Experimentally, we show that this metric can detect subtle yet semantically meaningful distribution shifts, such as the shift from healthy liver CT scans to those with tumors, and generalizes across different types of diffusion models, datasets, and inverse problems. Our code can be found at https://github.com/voilalab/KLIP.