Healthcare guided by semantic segmentation has the potential to improve our quality of life through early and accurate disease detection. Convolutional Neural Networks, especially the U-Net-based architectures, are currently the state-of-the-art learning-based segmentation methods and have given unprecedented performances. However, their decision-making processes are still an active field of research. In order to reliably utilize such methods in healthcare, explainability of how the segmentation was performed is mandated. To date, explainability is studied and applied heavily in classification tasks. In this work, we propose the Chimeric U-Net, a U-Net architecture with an invertible decoder unit, that inherently brings explainability into semantic segmentation tasks. We find that having the restriction of an invertible decoder does not hinder the performance of the segmentation task. However, the invertible decoder helps to disentangle the class information in the latent space embedding and to construct meaningful saliency maps. Furthermore, we found that with a simple k-Nearest-Neighbours classifier, we could predict the Intersection over Union scores of unseen data, demonstrating that the latent space, constructed by the Chimeric U-Net, encodes an interpretable representation of the segmentation quality. Explainability is an emerging field, and in this work, we propose an alternative approach, that is, rather than building tools for explaining a generic architecture, we propose constraints on the architecture which induce explainability. With this approach, we could peer into the architecture to reveal its class correlations and local contextual dependencies, taking an insightful step towards trustworthy and reliable AI. Code to build and utilize the Chimeric U-Net is made available under:

中文翻译：

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Chimeric U-Net – 修改标准 U-Net 以实现可解释性


以语义分割为指导的医疗保健有可能通过早期和准确的疾病检测来改善我们的生活质量。卷积神经网络，尤其是基于 U-Net 的架构，是目前最先进的基于学习的分割方法，并提供了前所未有的性能。然而，他们的决策过程仍然是一个活跃的研究领域。为了在医疗保健中可靠地利用此类方法，必须解释如何执行细分。迄今为止，可解释性在分类任务中被大量研究和应用。在这项工作中，我们提出了 Chimeric U-Net，这是一种具有可逆解码器单元的 U-Net 架构，它本质上为语义分割任务带来了可解释性。我们发现，具有可逆解码器的限制并不会妨碍分割任务的执行。然而，可逆解码器有助于解开潜在空间嵌入中的类信息，并构建有意义的显著性图。此外，我们发现，使用一个简单的 k-Nearest-Neighbours 分类器，我们可以预测看不见的数据的交集与联合分数，这表明由嵌合 U-Net 构建的潜在空间编码了分割质量的可解释表示。可解释性是一个新兴领域，在这项工作中，我们提出了一种替代方法，即，我们不是构建用于解释通用架构的工具，而是对架构提出约束，从而诱导可解释性。通过这种方法，我们可以窥视架构以揭示其类相关性和局部上下文依赖关系，从而朝着值得信赖和可靠的 AI 迈出有洞察力的一步。构建和使用 Chimeric U-Net 的代码可在以下位置获得：