Abstract
Optical microscopy demonstrated an incredible power and ability in producing large data set originated from biological samples by light interrogation and tunable in terms of spatial at temporal resolution down to the nano- and pico- scale, respectively. Such a data set is the core for developing an artificial microscope aiming to transform a label-free interrogation of the sample into a molecular-rich fluorescence-based image. The intelligent artificial microscope is AI-guided through a computational core based on three modules based on a convolutional neural network (CNN) and a tensor independent component analysis (tICA) un-supervised machine learning within a supervised deep learning strategy having the ambitious target to create a robust virtual environment "to see "what we could not perceive before". An interesting case study is related to understanding the visualisation of chromatin organisation.
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