  Multiphoton microscopy (MPM) is a powerful imaging tool that has been a
critical enabler for live tissue imaging. However, since most multiphoton
microscopy platforms rely on point scanning, there is an inherent trade-off
between acquisition time, field of view (FOV), phototoxicity, and image
quality, often resulting in noisy measurements when fast, large FOV, and/or
gentle imaging is needed. Deep learning could be used to denoise multiphoton
microscopy measurements, but these algorithms can be prone to hallucination,
which can be disastrous for medical and scientific applications. We propose a
method to simultaneously denoise and predict pixel-wise uncertainty for
multiphoton imaging measurements, improving algorithm trustworthiness and
providing statistical guarantees for the deep learning predictions.
Furthermore, we propose to leverage this learned, pixel-wise uncertainty to
drive an adaptive acquisition technique that rescans only the most uncertain
regions of a sample. We demonstrate our method on experimental noisy MPM
measurements of human endometrium tissues, showing that we can maintain fine
features and outperform other denoising methods while predicting uncertainty at
each pixel. Finally, with our adaptive acquisition technique, we demonstrate a
120X reduction in acquisition time and total light dose while successfully
recovering fine features in the sample. We are the first to demonstrate
distribution-free uncertainty quantification for a denoising task with real
experimental data and the first to propose adaptive acquisition based on
reconstruction uncertainty
