27 research outputs found
Machine learning methods for histopathological image analysis
Abundant accumulation of digital histopathological images has led to the
increased demand for their analysis, such as computer-aided diagnosis using
machine learning techniques. However, digital pathological images and related
tasks have some issues to be considered. In this mini-review, we introduce the
application of digital pathological image analysis using machine learning
algorithms, address some problems specific to such analysis, and propose
possible solutions.Comment: 23 pages, 4 figure
AI-based analysis of super-resolution microscopy: Biological discovery in the absence of ground truth
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.Comment: 14 pages, 3 figure