  Learning robust representations to discriminate cell phenotypes based on
microscopy images is important for drug discovery. Drug development efforts
typically analyse thousands of cell images to screen for potential treatments.
Early works focus on creating hand-engineered features from these images or
learn such features with deep neural networks in a fully or weakly-supervised
framework. Both require prior knowledge or labelled datasets. Therefore,
subsequent works propose unsupervised approaches based on generative models to
learn these representations. Recently, representations learned with
self-supervised contrastive loss-based methods have yielded state-of-the-art
results on various imaging tasks compared to earlier unsupervised approaches.
In this work, we leverage a contrastive learning framework to learn appropriate
representations from single-cell fluorescent microscopy images for the task of
Mechanism-of-Action classification. The proposed work is evaluated on the
annotated BBBC021 dataset, and we obtain state-of-the-art results in NSC, NCSB
and drop metrics for an unsupervised approach. We observe an improvement of 10%
in NCSB accuracy and 11% in NSC-NSCB drop over the previously best unsupervised
method. Moreover, the performance of our unsupervised approach ties with the
best supervised approach. Additionally, we observe that our framework performs
well even without post-processing, unlike earlier methods. With this, we
conclude that one can learn robust cell representations with contrastive
learning.
