  The Lucid methods described by Olah et al. (2018) provide a way to inspect
the inner workings of neural networks trained on image classification tasks
using feature visualization. Such methods have generally been applied to
networks trained on visually rich, large-scale image datasets like ImageNet,
which enables them to produce enticing feature visualizations. To investigate
these methods further, we applied them to classifiers trained to perform the
much simpler (in terms of dataset size and visual richness), yet challenging
task of distinguishing between different kinds of white blood cell from
microscope images. Such a task makes generating useful feature visualizations
difficult, as the discriminative features are inherently hard to identify and
interpret. We address this by presenting the "Illuminated Decision Tree"
approach, in which we use a neural network trained on the task as a feature
extractor, then learn a decision tree based on these features, and provide
Lucid visualizations for each node in the tree. We demonstrate our approach
with several examples, showing how this approach could be useful both in model
development and debugging, and when explaining model outputs to non-experts.

Computer Vision and Pattern Recognition;Human-Computer Interaction;Machine Learning