In this study, we assess the impact of using a foundation model for downstream cell classification model development, focusing on maximum model prediction performance and training data requirements. We found that using our foundation model improves the performance of fine-tuned histopathology algorithms requiring significantly less training annotations to reach peak performance and showing increased balanced accuracy compared to traditional approaches.

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