Deep learning architectures based on convolutional neural networks tend to rely on continuous, smooth features. While this characteristics provides significant robustness and proves useful in many real-world tasks, it is strikingly incompatible with the physical characteristic of the world, which, at the scale in which humans operate, comprises crisp objects, typically representing well-defined categories. This study proposes a class of neurosymbolic systems that learn by reconstructing images in terms of visual primitives and are thus forced to form high-level, structural explanations of them. When applied to the task of diagnosing abnormalities in histological imaging, the method proved superior to a conventional deep learning architecture in terms of classification accuracy, while being more transparent.

翻译：基于卷积神经网络的深度学习架构通常依赖于连续平滑的特征。虽然这一特性提供了显著的鲁棒性并在许多现实任务中被证明有效，但其与物理世界的特性存在显著矛盾——在人类活动的尺度上，世界由清晰的物体构成，这些物体通常代表明确定义的类别。本研究提出一类神经符号系统，其通过视觉基元重构图像进行学习，从而被迫形成对图像的高层次结构化解释。当应用于组织学影像异常诊断任务时，该方法在分类准确率上优于传统深度学习架构，同时具有更高的可解释性。