Bridging Generalization Gaps in High Content Imaging Through Online
  Self-Supervised Domain Adaptation
Johan Fredin Haslum Christos Matsoukas Karl-Johan Leuchowius Kevin Smith
High Content Imaging (HCI) plays a vital role in modern drug discovery and
development pipelines, facilitating various stages from hit identification to
candidate drug characterization. Applying machine learning models to these
datasets can prove challenging as they typically consist of multiple batches,
affected by experimental variation, especially if different imaging equipment
have been used. Moreover, as new data arrive, it is preferable that they are
analyzed in an online fashion. To overcome this, we propose CODA, an online
self-supervised domain adaptation approach. CODA divides the classifier's role
into a generic feature extractor and a task-specific model. We adapt the
feature extractor's weights to the new domain using cross-batch
self-supervision while keeping the task-specific model unchanged. Our results
demonstrate that this strategy significantly reduces the generalization gap,
achieving up to a 300% improvement when applied to data from different labs
utilizing different microscopes. CODA can be applied to new, unlabeled
out-of-domain data sources of different sizes, from a single plate to multiple
experimental batches.