  How can we find interpretable, domain-appropriate models of natural phenomena
given some complex, raw data such as images? Can we use such models to derive
scientific insight from the data? In this paper, we propose some methods for
achieving this. In particular, we implement disentangled representation
learning, sparse deep neural network training and symbolic regression, and
assess their usefulness in forming interpretable models of complex image data.
We demonstrate their relevance to the field of bioimaging using a well-studied
test problem of classifying cell states in microscopy data. We find that such
methods can produce highly parsimonious models that achieve $\sim98\%$ of the
accuracy of black-box benchmark models, with a tiny fraction of the complexity.
We explore the utility of such interpretable models in producing scientific
explanations of the underlying biological phenomenon.
