ArticleNature machine intelligence2023
Resolution enhancement with a task-assisted GAN to guide optical nanoscopy image analysis and acquisition.
Article in Nature machine intelligence, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 19 papers.
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Who cites it
19 citing papers in PubMed, 38 citations in OpenAlex.
- AI-empowered super-resolution microscopy: a revolution in nanoscale cellular imaging.Nature methods · 2026Review
- Smart event-triggered MINFLUX microscopy to catch and follow rare events.Nature communications · 2026Article
- Comparison of conventional 512-matrix CT images with Swin2SR-based 2048-matrix images in the visualization and diagnosis of lung nodules.Japanese journal of radiology · 2026Article
- Quantitative Stimulated Emission Depletion (STED) Microscopy with DNA-Fluorophore Labels.ACS nano · 2026Article
- Self-adaptive fine-tuning of deep learning super-resolution microscopy for artifact suppression in live-cell imaging.Innovation (Cambridge (Mass.)) · 2026Article
- DeBCR: a sparsity-efficient framework for image enhancement through a deep-learning-based solution to inverse problems.Communications engineering · 2026Article
- A flexible framework for automated STED super-resolution microscopy.Scientific reports · 2026Article
- Self-Driving Microscopes: AI Meets Super-Resolution Microscopy.Small methods · 2026Review
- Artificial Neural Networks for Bioimage Analysis.Methods in molecular biology (Clifton, N.J.) · 2026Article
- Bayesian reconstruction of rapidly scanned mid-infrared optoacoustic signals enables fast, label-free chemical microscopy.Science advances · 2025Article
- Preserving spatial and quantitative information in unpaired biomedical image-to-image translation.Cell reports methods · 2025Article
- Computational Super-Resolution: An Odyssey in Harnessing Priors to Enhance Optical Microscopy Resolution.Analytical chemistry · 2025Review
- Designing nanotheranostics with machine learning.Nature nanotechnology · 2024Review
- The rise of data-driven microscopy powered by machine learning.Journal of microscopy · 2024Article
- Harnessing artificial intelligence to reduce phototoxicity in live imaging.Journal of cell science · 2024Article
- Reconstruction of super-resolution from high-resolution remote sensing images based on convolutional neural networks.PeerJ. Computer science · 2024Article
- Development of AI-assisted microscopy frameworks through realistic simulation with pySTED.Nature machine intelligence · 2024Article
- GAN-WGCNA: Calculating gene modules to identify key intermediate regulators in cocaine addiction.PloS one · 2024Article
- Addressing annotation and data scarcity when designing machine learning strategies for neurophotonics.Neurophotonics · 2023Article
Corrections and comments
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Authors and funding
7 authors at 2 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Super-resolution fluorescence microscopy methods enable the characterization of nanostructures in living and fixed biological tissues. However, they require the adjustment of multiple imaging parameters while attempting to satisfy conflicting objectives, such as maximizing spatial and temporal resolution while minimizing light exposure. To overcome the limitations imposed by these trade-offs, post-acquisition algorithmic approaches have been proposed for resolution enhancement and image-quality improvement. Here we introduce the task-assisted generative adversarial network (TA-GAN), which incorporates an auxiliary task (for example, segmentation, localization) closely related to the observed biological nanostructure characterization. We evaluate how the TA-GAN improves generative accuracy over unassisted methods, using images acquired with different modalities such as confocal, bright-field, stimulated emission depletion and structured illumination microscopy. The TA-GAN is incorporated directly into the acquisition pipeline of the microscope to predict the nanometric content of the field of view without requiring the acquisition of a super-resolved image. This information is used to automatically select the imaging modality and regions of interest, optimizing the acquisition sequence by reducing light exposure. Data-driven microscopy methods like the TA-GAN will enable the observation of dynamic molecular processes with spatial and temporal resolutions that surpass the limits currently imposed by the trade-offs constraining super-resolution microscopy.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.