Using SILMAS to improve machine learning-assisted quantification of pathology
In this study, we investigate the benefits of using structured illumination light-sheet microscopy with axial sweeping (SILMAS) in the context of neural network-assisted quantification of pathology in volumetric data. A bottleneck for training a neural network is the availability of manually labeled training data that adequately covers the variance in unseen samples, allowing the network to correc
