Guided backprop interpretation for multilayer perceptron


I want to use interpretation algorithm for simple feed-forward neural network (multilayer perceptron with 13 input neurons, 2 layers deep - 10 neurons each, output is 2 class) to classify voxels.

Each input neuron comes as voxel from 13 feature maps channels of the CT image (extracted statistical maps). The question is can I use Guided Backpropagation for interpretation? Or it better to use model-agnostic methods like LIME?

I didn’t find any use cases of guided backpropagation for MLP, most of the examples are used with deep convolutional NNs.