ReviewBiophysical reviews2022
Deep learning-based image processing in optical microscopy.
Review in Biophysical reviews, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 37 papers.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
37 citing papers in PubMed.
- Physics-informed self-supervised denoising for fluorescence microscopy via structure and intensity priors.iScience · 2026Article
- Accelerated discovery of cell migration regulators using label-free deep learning-based automated tracking.Science advances · 2026Article
- FAST - filamentous actin segmentation tool for quantifying cytoskeletal organization.Journal of cell science · 2026Article
- Novel convolutional neural network for bacterial identification of confocal microscopic datasets.Scientific reports · 2026Article
- Self-Driving Microscopes: AI Meets Super-Resolution Microscopy.Small methods · 2026Review
- Effective transfer of tumor annotations from hematoxylin and eosin to fluorescence images of breast and lung tissues.Journal of biomedical optics · 2026Article
- AIstain: Enhancing microglial phagocytosis analysis through deep learning.Cell reports methods · 2025Article
- Advancing Multicolor Super-Resolution Volume Imaging: Illuminating Complex Cellular Dynamics.JACS Au · 2025Review
- Microscope Upcycling: Transforming legacy microscopes into automated cloud-integrated imaging systems.HardwareX · 2025Article
- Cyber-physical security of biochips: A perspective.Biomicrofluidics · 2025Article
- Article
- Applications of Artificial Intelligence, Deep Learning, and Machine Learning to Support the Analysis of Microscopic Images of Cells and Tissues.Journal of imaging · 2025Review
- Quantifying Monomer-Dimer Distribution of Nanoparticles from Uncorrelated Optical Images Using Deep Learning.ACS omega · 2025Article
- Enhancing AI microscopy for foodborne bacterial classification using adversarial domain adaptation to address optical and biological variability.Frontiers in artificial intelligence · 2025Article
- Effect of shear rate on earlyBiofilm · 2024Article
- Image processing tools for petabyte-scale light sheet microscopy data.Nature methods · 2024Article
- Ex vivo nanoscale abluminal mapping of putative cargo receptors at the blood-brain barrier of expanded brain capillaries.Fluids and barriers of the CNS · 2024Article
- Convolutional neural network transformer (CNNT) for fluorescence microscopy image denoising with improved generalization and fast adaptation.Scientific reports · 2024Article
- Deep learning-based spectroscopic single-molecule localization microscopy.Journal of biomedical optics · 2024Article
- Expanding the Horizons of Machine Learning in Nanomaterials to Chiral Nanostructures.Advanced materials (Deerfield Beach, Fla.) · 2024Review
Corrections and comments
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Authors and funding
6 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Optical microscopy has emerged as a key driver of fundamental research since it provides the ability to probe into imperceptible structures in the biomedical world. For the detailed investigation of samples, a high-resolution image with enhanced contrast and minimal damage is preferred. To achieve this, an automated image analysis method is preferable over manual analysis in terms of both speed of acquisition and reduced error accumulation. In this regard, deep learning (DL)-based image processing can be highly beneficial. The review summarises and critiques the use of DL in image processing for the data collected using various optical microscopic techniques. In tandem with optical microscopy, DL has already found applications in various problems related to image classification and segmentation. It has also performed well in enhancing image resolution in smartphone-based microscopy, which in turn enablse crucial medical assistance in remote places. Graphical abstract:
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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.