Abstract：
Histological images are frequently impaired by local artifacts from scanner malfunctions or iatrogenic processes - caused by preparation - impacting the performance of Deep Learning models. Models often struggle with the slightest out-of-distribution shifts, resulting in compromised performance. Detecting artifacts and failure modes of the models is crucial to ensure open-world applicability to whole slide images for tasks like segmentation or diagnosis. We introduce a novel technique for out-of-distribution detection within whole slide images, compatible with any segmentation or classification model. Our approach tiles multi-layer features into sliding window patches and leverages optimal transport to align them with recognized in-distribution samples. We average the optimal transport costs over tiles and layers to detect out-of-distribution samples. Notably, our method excels in identifying failure modes that would harm downstream performance, surpassing contemporary out-of-distribution detection techniques. We evaluate our method for both natural and synthetic artifacts, considering distribution shifts of various sizes and types. The results confirm that our technique outperforms alternative methods for artifact detection. We assess our method components and the ability to negate the impact of artifacts on the downstream tasks. Finally, we demonstrate that our method can mitigate the risk of performance drops in downstream tasks, enhancing reliability by up to 77%. In testing 7 annotated whole slide images with natural artifacts, our method boosted the Dice score by 68%, highlighting its real open-world utility.
摘要：
组织学图像经常受到扫描仪故障或医源性过程的局部伪影的损害-由准备引起-影响深度学习模型的性能。模型经常在最轻微的分布外变化中挣扎，导致性能受损。检测模型的伪影和故障模式对于确保对于像分割或诊断这样的任务对整个幻灯片图像的开放世界适用性至关重要。我们介绍了一种在整个幻灯片图像中进行分布外检测的新技术，与任何分割或分类模型兼容。我们的方法将多层功能拼贴到滑动窗口补丁中，并利用最佳传输将其与公认的分布样本对齐。我们对瓷砖和图层的最佳运输成本进行平均，以检测分布外的样本。值得注意的是,我们的方法擅长识别会损害下游性能的故障模式，超越当代分布式检测技术。我们评估我们的方法对天然和合成文物，考虑各种大小和类型的分布变化。结果证实我们的技术优于用于伪影检测的替代方法。我们评估了我们的方法组件以及消除工件对下游任务的影响的能力。最后,我们证明了我们的方法可以减轻下游任务性能下降的风险，可靠性提高高达77%。在测试7个带有自然伪影的带注释的整个幻灯片图像时，我们的方法提高了68%的骰子得分，突出其真正的开放世界效用。
