  Analyzing the morphology of cells in microscopy images can provide insights
into the mechanism of compounds or the function of genes. Addressing this task
requires methods that can not only extract biological information from the
images, but also ignore technical variations, ie, changes in experimental
procedure or differences between equipments used to collect microscopy images.
We propose Treatment ExemplArs with Mixture-of-experts (TEAMs), an embedding
learning approach that learns a set of experts that are specialized in
capturing technical variations in our training set and then aggregates
specialist's predictions at test time. Thus, TEAMs can learn powerful
embeddings with less technical variation bias by minimizing the noise from
every expert. To train our model, we leverage Treatment Exemplars that enable
our approach to capture the distribution of the entire dataset in every
minibatch while still fitting into GPU memory. We evaluate our approach on
three datasets for tasks like drug discovery, boosting performance on
identifying the true mechanism of action of cell treatments by 5.5-11% over the
state-of-the-art.
