Convolutional neural networks (CNNs) achieve prevailing results in segmentation tasks nowadays and represent the state-of-the-art for image-based analysis. However, the understanding of the accurate decision-making process of a CNN is rather unknown. The research area of explainable artificial intelligence (xAI) primarily revolves around understanding and interpreting this black-box behavior. One way of interpreting a CNN is the use of class activation maps (CAMs) that represent heatmaps to indicate the importance of image areas for the prediction of the CNN. For classification tasks, a variety of CAM algorithms exist. But for segmentation tasks, only one CAM algorithm for the interpretation of the output of a CNN exist. We propose a transfer between existing classification- and segmentation-based methods for more detailed, explainable, and consistent results which show salient pixels in semantic segmentation tasks. The resulting Seg-HiRes-Grad CAM is an extension of the segmentation-based Seg-Grad CAM with the transfer to the classification-based HiRes CAM. Our method improves the previously-mentioned existing segmentation-based method by adjusting it to recently published classification-based methods. Especially for medical image segmentation, this transfer solves existing explainability disadvantages.

翻译：卷积神经网络（CNN）在当今分割任务中取得了主导性成果，代表了基于图像分析的最先进技术。然而，对CNN精确决策过程的理解仍较为欠缺。可解释人工智能（xAI）的研究领域主要围绕理解和解释这种黑箱行为展开。解释CNN的一种方法是使用类别激活图（CAMs），该类热力图用于指示图像区域对CNN预测的重要性。针对分类任务，已存在多种CAM算法。但对于分割任务，目前仅有一种用于解释CNN输出的CAM算法。本文提出将现有基于分类与基于分割的方法进行迁移，以获得更详细、可解释且一致的结果，从而在语义分割任务中凸显显著像素。所提出的Seg-HiRes-Grad CAM是基于分割的Seg-Grad CAM的扩展，并融合了基于分类的HiRes CAM的迁移特性。我们的方法通过适配近期发表的基于分类的方法，改进了前述现有的基于分割方法。特别是在医学图像分割领域，这种迁移解决了现有可解释性方法的不足。