AI-based analysis of super-resolution microscopy: Biological discovery
  in the absence of ground truth
Ivan R. Nabi Ben Cardoen Ismail M. Khater Guang Gao Timothy H. Wong Ghassan Hamarneh
The nanoscale resolution of super-resolution microscopy has now enabled the
use of fluorescent based molecular localization tools to study whole cell
structural biology. Machine learning based analysis of super-resolution data
offers tremendous potential for discovery of new biology, that by definition is
not known and lacks ground truth. Herein, we describe the application of weakly
supervised learning paradigms to super-resolution microscopy and its potential
to enable the accelerated exploration of the molecular architecture of
subcellular macromolecules and organelles.