论文标题
了解您的空间：用于校准医疗OOD探测器的内部和离群构造
Know Your Space: Inlier and Outlier Construction for Calibrating Medical OOD Detectors
论文作者
论文摘要
我们专注于生产良好的分布（OOD）检测器的问题，以便能够安全部署医疗图像分类器。由于难以策划合适的校准数据集的激励，合成的增强已变得非常普遍，因为inlier/terlier suilfer规范。尽管数据增强技术已经取得了迅速的进步，但本文引人注目的发现，除了增强的类型外，还合成了嵌入式和异常值的空间在校准OOD探测器中起着至关重要的作用。使用流行的基于能量的OOD检测框架，我们发现最佳协议是合成潜在空间嵌入式与不同的像素空间异常值一起。基于具有多个医学成像基准测试的实证研究，我们证明我们的方法始终导致在各种开放式识别设置中，与最先进的ART相比，与最先进的ART相比，OOD检测（$ 15 \％-35 \％$）。
We focus on the problem of producing well-calibrated out-of-distribution (OOD) detectors, in order to enable safe deployment of medical image classifiers. Motivated by the difficulty of curating suitable calibration datasets, synthetic augmentations have become highly prevalent for inlier/outlier specification. While there have been rapid advances in data augmentation techniques, this paper makes a striking finding that the space in which the inliers and outliers are synthesized, in addition to the type of augmentation, plays a critical role in calibrating OOD detectors. Using the popular energy-based OOD detection framework, we find that the optimal protocol is to synthesize latent-space inliers along with diverse pixel-space outliers. Based on empirical studies with multiple medical imaging benchmarks, we demonstrate that our approach consistently leads to superior OOD detection ($15\% - 35\%$ in AUROC) over the state-of-the-art in a variety of open-set recognition settings.
