AIMC Topic: Microscopy, Fluorescence
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Applications of machine learning in time-domain fluorescence lifetime imaging: a review.
Methods and applications in fluorescence
Feb 8, 2024
Many medical imaging modalities have benefited from recent advances in Machine Learning (ML), specifically in deep learning, such as neural networks. Computers can be trained to investigate and enhance medical imaging methods without using valuable h...
Harnessing artificial intelligence to reduce phototoxicity in live imaging.
Journal of cell science
Feb 7, 2024
Fluorescence microscopy is essential for studying living cells, tissues and organisms. However, the fluorescent light that switches on fluorescent molecules also harms the samples, jeopardizing the validity of results - particularly in techniques suc...
Retrospective validation of MetaSystems' deep-learning-based digital microscopy platform with assistance compared to manual fluorescence microscopy for detection of mycobacteria.
Journal of clinical microbiology
Feb 1, 2024
UNLABELLED: This study aimed to validate Metasystems' automated acid-fast bacilli (AFB) smear microscopy scanning and deep-learning-based image analysis module (Neon Metafer) with assistance on respiratory and pleural samples, compared to conventiona...
Cellular nucleus image-based smarter microscope system for single cell analysis.
Biosensors & bioelectronics
Jan 18, 2024
Cell imaging technology is undoubtedly a powerful tool for studying single-cell heterogeneity due to its non-invasive and visual advantages. It covers microscope hardware, software, and image analysis techniques, which are hindered by low throughput ...
Pattern recognition in the nucleation kinetics of non-equilibrium self-assembly.
Nature
Jan 17, 2024
Inspired by biology's most sophisticated computer, the brain, neural networks constitute a profound reformulation of computational principles. Analogous high-dimensional, highly interconnected computational architectures also arise within information...
From pixels to insights: Machine learning and deep learning for bioimage analysis.
BioEssays : news and reviews in molecular, cellular and developmental biology
Dec 6, 2023
Bioimage analysis plays a critical role in extracting information from biological images, enabling deeper insights into cellular structures and processes. The integration of machine learning and deep learning techniques has revolutionized the field, ...
Deep UV-excited fluorescence microscopy installed with CycleGAN-assisted image translation enhances precise detection of lymph node metastasis towards rapid intraoperative diagnosis.
Scientific reports
Dec 4, 2023
Rapid and precise intraoperative diagnosing systems are required for improving surgical outcomes and patient prognosis. Because of the poor quality and time-intensive process of the prevalent frozen section procedure, various intraoperative diagnosti...
FiCRoN, a deep learning-based algorithm for the automatic determination of intracellular parasite burden from fluorescence microscopy images.
Medical image analysis
Nov 19, 2023
Protozoan parasites are responsible for dramatic, neglected diseases. The automatic determination of intracellular parasite burden from fluorescence microscopy images is a challenging problem. Recent advances in deep learning are transforming this pr...
Automated quantification of vacuole fusion and lipophagy in from fluorescence and cryo-soft X-ray microscopy data using deep learning.
Autophagy
Oct 31, 2023
During starvation in the yeast vacuolar vesicles fuse and lipid droplets (LDs) can become internalized into the vacuole in an autophagic process named lipophagy. There is a lack of tools to quantitatively assess starvation-induced vacuole fusion and...
Live-cell imaging in the deep learning era.
Current opinion in cell biology
Oct 27, 2023
Live imaging is a powerful tool, enabling scientists to observe living organisms in real time. In particular, when combined with fluorescence microscopy, live imaging allows the monitoring of cellular components with high sensitivity and specificity....
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