This paper introduces feature gradient flow, a new technique for interpreting
deep learning models in terms of features that are understandable to humans.
The gradient flow of a model locally defines nonlinear coordinates in the input
data space representing the information the model is using to make its
decisions. Our idea is to measure the agreement of interpretable features with
the gradient flow of a model. To then evaluate the importance of a particular
feature to the model, we compare that feature's gradient flow measure versus
that of a baseline noise feature. We then develop a technique for training
neural networks to be more interpretable by adding a regularization term to the
loss function that encourages the model gradients to align with those of chosen
interpretable features. We test our method in a convolutional neural network
prediction of distant metastasis of head and neck cancer from a computed
tomography dataset from the Cancer Imaging Archive.

该研究介绍了一种名为特征梯度流的新技术，用于以人类可以理解的特征来解释深度学习模型。该技术通过测量可解释特征与模型的梯度流之间的一致性来评估特定特征对模型的重要性，并通过在损失函数中添加正则项来训练更易解释的神经网络。研究还在从癌症影像存档中的计算机断层扫描数据集中，对头颈癌远处转移的卷积神经网络预测进行了测试。