Machine learning in microscopy–insights, opportunities and challenges
Machine learning (ML) is transforming the field of image processing and analysis, from
automation of laborious tasks to open-ended exploration of visual patterns. This has striking …
automation of laborious tasks to open-ended exploration of visual patterns. This has striking …
AI analysis of super-resolution microscopy: Biological discovery in the absence of ground truth
Super-resolution microscopy, or nanoscopy, enables the use of fluorescent-based molecular
localization tools to study molecular structure at the nanoscale level in the intact cell …
localization tools to study molecular structure at the nanoscale level in the intact cell …
Image analysis optimization for nanowire-based optical detection of molecules
Semiconductor nanowires can enhance the signal of fluorescent molecules, thus
significantly improving the limits of fluorescence detection in optical biosensing. In this work …
significantly improving the limits of fluorescence detection in optical biosensing. In this work …
Self‐Driving Microscopes: AI Meets Super‐Resolution Microscopy
EN Ward, A Scheeder, M Barysevich… - Small …, 2025 - Wiley Online Library
Abstract The integration of Machine Learning (ML) with super‐resolution microscopy
represents a transformative advancement in biomedical research. Recent advances in ML …
represents a transformative advancement in biomedical research. Recent advances in ML …
Machine Learning‐Based Estimation of Experimental Artifacts and Image Quality in Fluorescence Microscopy
Reliable characterization of image data is fundamental for imaging applications, FAIR data
management, and an objective evaluation of image acquisition, processing, and analysis …
management, and an objective evaluation of image acquisition, processing, and analysis …
VNC-Dist: A machine learning-based tool for quantification of neuronal positioning in the ventral nerve cord of C. elegans
The ventral nerve cord (VNC) of newly hatched C. elegans contains 22 motoneurons
organized into three distinct classes: DD, DA, and DB, that show stereotypical positioning …
organized into three distinct classes: DD, DA, and DB, that show stereotypical positioning …
CelFDrive: Artificial Intelligence assisted microscopy for automated detection of rare events
S Brooks, S Toral-Perez, DS Corcoran, K Kilborn… - bioRxiv, 2024 - biorxiv.org
CelFDrive automates high-resolution 3D imaging cells of interest across a variety of
fluorescence microscopes, integrating deep learning cell classification from auxiliary low …
fluorescence microscopes, integrating deep learning cell classification from auxiliary low …