用视觉原语重构图像，让模型更懂医学影像的结构本质
Autoassociative Learning of Structural Representations for Modeling and Classification in Medical Imaging
- 通过重建图像中的视觉原语学习结构化表示
- 在病理图像分类中准确率高于传统深度模型
- 结果更可解释，适合需要透明决策的医疗场景
基于卷积神经网络的深度学习模型通常依赖连续平滑特征，虽具鲁棒性，却与人类操作尺度下世界由清晰物体构成的物理特性相悖。本文提出一类神经符号系统，通过图像的视觉原语重构来学习，从而强制生成高层次的结构性解释。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，且具备更强的可解释性。
原文摘要 · Abstract (English)
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.
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