Deep learning based phase imaging with uncertainty quantification

Emerging deep learning based computational microscopy techniques promise novel imaging capabilities beyond traditional techniques. In this talk, I will discuss our recent efforts in building such techniques that provide improved scalability and reliability. I will demonstrate a physics guided deep learning imaging approach that enables designing highly efficient multiplexed data acquisition schemes and fully leverages the powerful deep learning-based inverse problem framework (Fig. 1a). We apply this approach to large space-bandwidth product phase microscopy using intensity-only measurements, implemented on a simple LED-array based computational microscopy platform [1] (Fig. 1b). The trained network is shown to be robust to sample variations and various experimental imperfections (Fig. 1c). I will discuss an uncertainty quantification framework to assess the reliability of the deep learning predictions. Quantifying the uncertainty provides per-pixel evaluation of the prediction?s confidence level as well as the quality of the model and dataset. This uncertainty learning framework is widely applicable to build reliable deep learning-based biomedical imaging techniques.