Title: Enhancing the Extraction of Interpretable Information for Ischemic Stroke Imaging from Deep Neural Networks Show Chinese title

Title: 增强从深度神经网络中提取可解释性信息的 ischemic stroke 成像

Abstract: We implement a visual interpretability method Layer-wise Relevance Propagation (LRP) on top of 3D U-Net trained to perform lesion segmentation on the small dataset of multi-modal images provided by ISLES 2017 competition. We demonstrate that LRP modifications could provide more sensible visual explanations to an otherwise highly noise-skewed saliency map. We also link amplitude of modified signals to useful information content. High amplitude localized signals appear to constitute the noise that undermines the interpretability capacity of LRP. Furthermore, mathematical framework for possible analysis of function approximation is developed by analogy. Show Chinese abstract

Abstract: 我们在ISLES 2017竞赛提供的多模态图像小数据集上训练的3D U-Net之上实现了视觉可解释性方法逐层相关性传播（LRP）。 我们证明了LRP修改可以为原本高度噪声偏斜的显著性图提供更有意义的视觉解释。 我们还将修改后的信号幅度与有用信息内容联系起来。 高幅度的局部信号似乎构成了损害LRP可解释能力的噪声。 此外，通过类比开发了可能用于函数逼近分析的数学框架。