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.

通过使用可解释的模型，我们研究如何从复杂图像数据中得出科学洞见，并通过实现解缠表示学习、稀疏深度神经网络训练和符号回归等方法，对复杂图像数据形成可解释的模型进行评估，以及探索这些可解释模型在揭示潜在生物现象方面的用途。