Deep Learning (DL) models tend to perform poorly when the data comes from a distribution different from the training one. In critical applications such as medical imaging, out-of-distribution (OOD) detection helps to identify such data samples, increasing the model's reliability. Recent works have developed DL-based OOD detection that achieves promising results on 2D medical images. However, scaling most of these approaches on 3D images is computationally intractable. Furthermore, the current 3D solutions struggle to achieve acceptable results in detecting even synthetic OOD samples. Such limited performance might indicate that DL often inefficiently embeds large volumetric images. We argue that using the intensity histogram of the original CT or MRI scan as embedding is descriptive enough to run OOD detection. Therefore, we propose a histogram-based method that requires no DL and achieves almost perfect results in this domain. Our proposal is supported two-fold. We evaluate the performance on the publicly available datasets, where our method scores 1.0 AUROC in most setups. And we score second in the Medical Out-of-Distribution challenge without fine-tuning and exploiting task-specific knowledge. Carefully discussing the limitations, we conclude that our method solves the sample-level OOD detection on 3D medical images in the current setting.

翻译：深度学习模型在数据来自与训练分布不同的分布时，往往表现不佳。在医学影像等关键应用中，分布外（OOD）检测有助于识别此类数据样本，从而提高模型的可靠性。近期研究已发展出基于深度学习的OOD检测方法，在二维医学图像上取得了可喜成果。然而，将这些方法扩展到三维图像在计算上大多难以实现。此外，现有三维解决方案在检测甚至合成OOD样本时也难以达到可接受的结果。这种性能限制可能表明，深度学习在嵌入大型体积图像时效率不足。我们认为，使用原始CT或MRI扫描的强度直方图作为嵌入已足够描述OOD检测。因此，我们提出一种基于直方图的方法，无需深度学习且在该领域达到近乎完美的结果。我们的方案得到双重支持：在公开数据集上的性能评估显示，该方法在大多数设置中AUROC得分为1.0；且在无需微调和利用任务特定知识的情况下，我们在医学分布外挑战赛中获得第二名。通过仔细讨论局限性，我们得出结论：在当前设定下，我们的方法解决了三维医学图像上的样本级OOD检测问题。