论文标题
从解释到细分：使用可解释的AI进行图像分割
From Explanations to Segmentation: Using Explainable AI for Image Segmentation
论文作者
论文摘要
图像分割的新时代利用深神经网（DNN）的力量带有价格标签：训练神经网络以进行像素的细分，必须在Pixel-Precision上手动标记大量训练样本。在这项工作中，我们通过遵循间接解决方案来解决此问题。我们基于可解释的AI（XAI）社区的进步，并从层面相关性传播的输出（LRP）中提取一个像素的二进制细分，从而解释了分类网络的决策。我们表明，与已建立的U-NET分割体系结构相比，我们获得了相似的结果，而训练数据的产生显着简化了。所提出的方法可以以弱监督的方式进行训练，因为训练样本必须仅在图像级别上标记，同时又可以实现分割掩码的输出。这使其特别适用于更广泛的真实应用程序，在这些应用程序中通常无法使用乏味的像素级标签。
The new era of image segmentation leveraging the power of Deep Neural Nets (DNNs) comes with a price tag: to train a neural network for pixel-wise segmentation, a large amount of training samples has to be manually labeled on pixel-precision. In this work, we address this by following an indirect solution. We build upon the advances of the Explainable AI (XAI) community and extract a pixel-wise binary segmentation from the output of the Layer-wise Relevance Propagation (LRP) explaining the decision of a classification network. We show that we achieve similar results compared to an established U-Net segmentation architecture, while the generation of the training data is significantly simplified. The proposed method can be trained in a weakly supervised fashion, as the training samples must be only labeled on image-level, at the same time enabling the output of a segmentation mask. This makes it especially applicable to a wider range of real applications where tedious pixel-level labelling is often not possible.
