Évènement

Abstract : Understanding intracellular processes requires accurately tracking individual molecules in live-cell microscopy data. This is challenging because imaging noise and high particle density create many possible trajectory assignments. We combine transformer-based attention mechanisms to identify the most likely trajectory hypotheses with Bayesian tracking to reconstruct particle motion. This hybrid approach improves robustness and doubles tracking performance compared with conventional methods.