AI-based analysis of super-resolution microscopy: Biological discovery in the absence of ground truth

2023/05/26 by Ivan R. Nabi, Ben Cardoen, Nabi, Ivan R. +9
Biochemistry, Genetics and Molecular Biology · Engineering · #Advanced Electron Microscopy Techniques and Applications #Advanced Fluorescence Microscopy Techniques #Artificial Intelligence (cs.AI) #Biological Physics (physics.bio-ph) #Computer Vision and Pattern Recognition (cs.CV) #FOS: Biological sciences #FOS: Computer and information sciences #FOS: Physical sciences #Image Processing Techniques and Applications #Machine Learning (cs.LG) #Quantitative Methods (q-bio.QM) #Subcellular Processes (q-bio.SC)

paper · pdf · doi:10.48550/arxiv.2305.17193

openalex publication_date 2023/05/26 · openalex created_date 2025/10/10 · openalex updated_date 2026/08/01

Abstract

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, bridging the mesoscale gap to classical structural biology methodologies. Analysis of super-resolution data by artificial intelligence (AI), such as machine learning, 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 paradigms to super-resolution microscopy and its potential to enable the accelerated exploration of the nanoscale architecture of subcellular macromolecules and organelles.

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