AI-based analysis of super-resolution microscopy: Biological discovery in the absence of ground truth
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Abstract (arXiv)
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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9 of 107 references.
References · 9
- 2016Grad-CAM: Visual Explanations from Deep Networks via Gradient-based Localizationcited by 6,733
- 2018Continual Lifelong Learning with Neural Networks: A Reviewcited by 1,330
- 2017CheXNet: Radiologist-Level Pneumonia Detection on Chest X-Rays with Deep Learningcited by 461
- 2018Generative Adversarial Network in Medical Imaging: A Reviewcited by 257
- 2021Can contrastive learning avoid shortcut solutions?cited by 114
- 2022Guidelines and Evaluation of Clinical Explainable AI in Medical Image Analysiscited by 35
- 2023Roadmap on Deep Learning for Microscopycited by 6
- 2021DEEMD: Drug Efficacy Estimation against SARS-CoV-2 based on cell Morphology with Deep multiple instance learningcited by 4
- 2021Advancing biological super-resolution microscopy through deep learning: a brief reviewcited by 2
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