Julie C. Wilson

This study uses live-cell imaging and automated phenotyping to characterize cancer-stromal interactions, revealing phenotypic changes in breast cancer cells exposed to cancer-associated fibroblasts.

The paper quantifies variability in manual and automated cell segmentation and tracking methods, showing their impact on phenotypic metrics and reproducibility.

The paper compares four deep-learning architectures for classifying crystallisation images, showing an ensemble approach achieves 94% accuracy in categorizing experimental outcomes.