  Recent advances in computer vision and robotics enabled automated large-scale
biological image analysis. Various machine learning approaches have been
successfully applied to phenotypic profiling. However, it remains unclear how
they compare in terms of biological feature extraction. In this study, we
propose a simple CNN architecture and implement 4 different representation
learning approaches. We train 16 deep learning setups on the 770k cancer cell
images dataset under identical conditions, using different augmenting and
cropping strategies. We compare the learned representations by evaluating
multiple metrics for each of three downstream tasks: i) distance-based
similarity analysis of known drugs, ii) classification of drugs versus
controls, iii) clustering within cell lines. We also compare training times and
memory usage. Among all tested setups, multi-crops and random augmentations
generally improved performance across tasks, as expected. Strikingly,
self-supervised (implicit contrastive learning) models showed competitive
performance being up to 11 times faster to train. Self-supervised regularized
learning required the most of memory and computation to deliver arguably the
most informative features. We observe that no single combination of augmenting
and cropping strategies consistently results in top performance across tasks
and recommend prospective research directions.
