Zhifeng Zhao

TL;DR: DeepCAD is developed, a self-supervised deep-learning method for spatiotemporal enhancement of calcium imaging data that does not require any high signal-to-noise ratio (SNR) observations and suppresses detection noise and improves the SNR more than tenfold, which reinforces the accuracy of neuron extraction and spike inference and facilitates the functional analysis of neural circuits.

TL;DR: A high-speed, multi-modal, label-free MPM by Bessel scan-based strip mosaicking that enables full axial information acquisition at once and alleviates the demanding requirement of sample alignment is proposed.

TL;DR: DeepCAD-RT as mentioned in this paper is a self-supervised deep learning method for real-time noise suppression in fluorescence microscopy, which reduces the number of network parameters by 94%, memory consumption by 27-fold and processing time by a factor of 20.

TL;DR: Yang et al. as mentioned in this paper harnessed the concept of synthetic aperture radar in TPM to achieve aberration-corrected 3D imaging of subcellular dynamics at a millisecond scale for over 100,000 large volumes in deep tissue, with three orders of magnitude reduction in photobleaching.

TL;DR: DeepCAD-RT as mentioned in this paper is a self-supervised method for effective noise suppression of fluorescence time-lapse imaging, which can facilitate the morphological and functional interrogation of biological dynamics with minimal photon budget.