Self-supervised learning has emerged as a powerful paradigm to bridge the gap between data abundance and label scarcity in medical imaging. By constructing supervisory signals from the data ...

In principle, ML/AI-based algorithms should enable rapid and accurate cell segmentation in high-throughput settings. However, reliance on large training datasets, human input, computational expertise, ...