  In this paper, we present contemporary techniques for visualising the feature
space of a deep learning image classification neural network. These techniques
are viewed in the context of a feed-forward network trained to classify low
resolution fluorescence images of white blood cells captured using optofluidic
imaging. The model has two output classes corresponding to two different cell
types, which are often difficult to distinguish by eye. This paper has two
major sections. The first looks to develop the information space presented by
dimension reduction techniques, such as t-SNE, used to embed high-dimensional
pre-softmax layer activations into a two-dimensional plane. The second section
looks at feature visualisation by optimisation to generate feature images
representing the learned features of the network. Using and developing these
techniques we visualise class separation and structures within the dataset at
various depths using clustering algorithms and feature images; track the
development of feature complexity as we ascend the network; and begin to
extract the features the network has learnt by modulating single-channel
feature images with up-scaled neuron activation maps to distinguish their most
salient parts.
