Claire McQuin

The paper develops a weakly supervised learning method to learn treatment effect representations from imaging data, improving downstream analysis performance.

This study uses deep learning and label-free imaging flow cytometry to classify red blood cell morphologies during storage, achieving 90% accuracy and predicting expiration dates based on morphological changes.

This study evaluates deep learning strategies for nucleus segmentation in fluorescence images, showing U-Net outperforms classical methods and reduces biologically relevant errors.

This paper develops a weakly supervised CNN approach for learning single-cell feature embeddings from microscopy images, achieving better accuracy than classical features and competitiveness with transfer learning methods.