KS-GNNExplainer：通过组织病理学图像上的实例解释实现全局模型解释
KS-GNNExplainer: Global Model Interpretation Through Instance Explanations On Histopathology images
AI总结：
本文提出KS-GNNExplainer，首个通过有效整合实例级解释方法从组织病理图像中提取全局模式的图神经网络解释器，弥补了现有实例级方法无法提供类别共同模式解释的不足。
摘要：
实例级图神经网络解释器已被证明有助于解释组织病理学图像上的此类网络。然而，目前很少有方法能够提供模型解释，即同一类别样本之间的共同模式。我们设想，基于图的组织病理图像分析可以从此类解释中显著受益。另一方面，当前的模型级解释器基于图生成方法，由于在现实世界中其生成的图没有对应的图像，因此这些方法不适用于该领域。因此，这类解释无法有效地传达给专家。为了实现这一愿景，我们开发了KS-GNNExplainer，这是第一个实例级图神经网络解释器，它有效地利用当前的实例级方法，提供更具信息量和可靠性的可解释输出，这对于健康领域的应用人工智能至关重要。我们在多个数据集上基于定量和定性指标的实验表明，所提出的解释器能够充当全局模式提取器，而这是当前该领域实例级方法的一个根本性局限。
英文摘要：
Instance-level graph neural network explainers have proven beneficial for explaining such networks on histopathology images. However, there has been few methods that provide model explanations, which are common patterns among samples within the same class. We envision that graph-based histopathological image analysis can benefit significantly from such explanations. On the other hand, current model-level explainers are based on graph generation methods that are not applicable in this domain because of no corresponding image for their generated graphs in real world. Therefore, such explanations are communicable to the experts. To follow this vision, we developed KS-GNNExplainer, the first instance-level graph neural network explainer that leverages current instance-level approaches in an effective manner to provide more informative and reliable explainable outputs, which are crucial for applied AI in the health domain. Our experiments on various datasets, and based on both quantitative and qualitative measures, demonstrate that the proposed explainer is capable of being a global pattern extractor, which is a fundamental limitation of current instance-level approaches in this domain.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
