Maximilian Josef Joas

AUTOENCODIX provides a standardized framework for training and evaluating autoencoders, demonstrating its utility across multi-modal biological data and offering insights into representation learning trade-offs.

This paper shows that using the Segment Anything Model (SAM) for zero-shot confluence estimation in mesenchymal stem cells is effective without needing extensive dataset curation or fine-tuning.