Machine learning-enabled quantitative phase imaging: my collaborations with Prof. Gabi Popescu

Quantitative phase imaging (QPI) has recently emerged as a potentially valuable label-free approach which, due to its high resolution and sensitivity, has enabled a broad range of new applications. However, being a label-free technique, structures in a QPI image of a cell cannot always be readily identified because the image lacks specificity. To address this, machine learning methods have been deployed to map an acquired QPI image to a different image type, such as a simulated fluorescence image or an image representing a labeled segmentation mask. In this talk, I review several productive collaborations I had with Prof. Gabi Popescu on this topic. The applications surveyed include live-dead assay on unlabeled cells, cell stage classification and artificial confocal microscopy for deep label-free imaging.