  Deep Learning models have been successfully utilized to extract clinically
actionable insights from routinely available histology data. Generally, these
models require annotations performed by clinicians, which are scarce and costly
to generate. The emergence of self-supervised learning (SSL) methods remove
this barrier, allowing for large-scale analyses on non-annotated data. However,
recent SSL approaches apply increasingly expansive model architectures and
larger datasets, causing the rapid escalation of data volumes, hardware
prerequisites, and overall expenses, limiting access to these resources to few
institutions. Therefore, we investigated the complexity of contrastive SSL in
computational pathology in relation to classification performance with the
utilization of consumer-grade hardware. Specifically, we analyzed the effects
of adaptations in data volume, architecture, and algorithms on downstream
classification tasks, emphasizing their impact on computational resources. We
trained breast cancer foundation models on a large public patient cohort and
validated them on various downstream classification tasks in a weakly
supervised manner on two external public patient cohorts. Our experiments
demonstrate that we can improve downstream classification performance whilst
reducing SSL training duration by 90%. In summary, we propose a set of
adaptations which enable the utilization of SSL in computational pathology in
non-resource abundant environments.
