AIMC Topic: Microscopy, Fluorescence
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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....
Super-resolution biomedical imaging via reference-free statistical implicit neural representation.
Physics in medicine and biology
Oct 16, 2023
Supervised deep learning for image super-resolution (SR) has limitations in biomedical imaging due to the lack of large amounts of low- and high-resolution image pairs for model training. In this work, we propose a reference-free statistical implicit...
Localization and phenotyping of tuberculosis bacteria using a combination of deep learning and SVMs.
Computers in biology and medicine
Oct 13, 2023
Successful treatment of pulmonary tuberculosis (TB) depends on early diagnosis and careful monitoring of treatment response. Identification of acid-fast bacilli by fluorescence microscopy of sputum smears is a common tool for both tasks. Microscopy-b...
Analysis of cortical cell polarity by imaging flow cytometry.
Journal of cellular biochemistry
Sep 18, 2023
Metastasis is the main cause of cancer-related death and therapies specifically targeting metastasis are highly needed. Cortical cell polarity (CCP) is a prometastatic property of circulating tumor cells affecting their ability to exit blood vessels ...
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