AIMC Topic: Microscopy

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Highly accurate differentiation of bone marrow cell morphologies using deep neural networks on a large image data set.

Blood Nov 18, 2021
Biomedical applications of deep learning algorithms rely on large expert annotated data sets. The classification of bone marrow (BM) cell cytomorphology, an important cornerstone of hematological diagnosis, is still done manually thousands of times e...
Hematologic Diseases Microscopy Image Processing, Computer-Assisted Neural Networks, Computer Bone Marrow Cells Cell Differentiation Humans
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CellProfiler Analyst 3.0: accessible data exploration and machine learning for image analysis.

Bioinformatics (Oxford, England) Nov 5, 2021
SUMMARY: Image-based experiments can yield many thousands of individual measurements describing each object of interest, such as cells in microscopy screens. CellProfiler Analyst is a free, open-source software package designed for the exploration of...
Image Processing, Computer-Assisted Neural Networks, Computer Microscopy Software Machine Learning
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Deep Phenotypic Cell Classification using Capsule Neural Network.

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference Nov 1, 2021
Recent developments in ultra-high-throughput microscopy have created a new generation of cell classification methodologies focused solely on image-based cell phenotypes. These image-based analyses enable morphological profiling and screening of thous...
Image Processing, Computer-Assisted Neural Networks, Computer Microscopy
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Blind microscopy image denoising with a deep residual and multiscale encoder/decoder network.

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference Nov 1, 2021
In computer-aided diagnosis (CAD) focused on microscopy, denoising improves the quality of image analysis. In general, the accuracy of this process may depend both on the experience of the microscopist and on the equipment sensitivity and specificity...
Microscopy Diagnosis, Computer-Assisted Image Processing, Computer-Assisted Sensitivity and Specificity Neural Networks, Computer
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Combining collective and artificial intelligence for global health diseases diagnosis using crowdsourced annotated medical images.

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference Nov 1, 2021
Visual inspection of microscopic samples is still the gold standard diagnostic methodology for many global health diseases. Soil-transmitted helminth infection affects 1.5 billion people worldwide, and is the most prevalent disease among the Neglecte...
Child Microscopy Global Health Crowdsourcing Humans Artificial Intelligence Neural Networks, Computer
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An Effective Deep Learning Framework for Cell Segmentation in Microscopy Images.

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference Nov 1, 2021
Cell segmentation is a common step in cell behavior analysis. Reliably and automatically segmenting cells in microscopy images remains challenging, especially in differential inference contrast microscopy images and phase-contrast microscopy images. ...
Deep Learning Image Processing, Computer-Assisted Humans HeLa Cells Microscopy
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Deep Learning-Assisted Multiphoton Microscopy to Reduce Light Exposure and Expedite Imaging in Tissues With High and Low Light Sensitivity.

Translational vision science & technology Oct 4, 2021
PURPOSE: Two-photon excitation fluorescence (2PEF) reveals information about tissue function. Concerns for phototoxicity demand lower light exposure during imaging. Reducing excitation light reduces the quality of the image by limiting fluorescence e...
Photophobia Microscopy Humans Deep Learning Magnetic Resonance Imaging Image Processing, Computer-Assisted
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Avoiding a replication crisis in deep-learning-based bioimage analysis.

Nature methods Oct 1, 2021
Deep learning algorithms are powerful tools to analyse, restore and transform bioimaging data, increasingly used in life sciences research. These approaches now outperform most other algorithms for a broad range of image analysis tasks. In particular...
Deep Learning Biomedical Research Computational Biology Image Processing, Computer-Assisted Microscopy
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DeepS: a web server for image optical sectioning and super resolution microscopy based on a deep learning framework.

Bioinformatics (Oxford, England) Sep 29, 2021
MOTIVATION: Microscopy technology plays important roles in many biological research fields. Solvent-cleared brain high-resolution (HR) 3D image reconstruction is an important microscopy application. However, 3D microscopy image generation is time-con...
Deep Learning Computers Imaging, Three-Dimensional Animals Image Processing, Computer-Assisted Mice Microscopy
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Auto-focusing and quantitative phase imaging using deep learning for the incoherent illumination microscopy system.

Optics express Aug 16, 2021
It is well known that the quantitative phase information which is vital in the biomedical study is hard to be directly obtained with bright-field microscopy under incoherent illumination. In addition, it is impossible to maintain the living sample in...
Microspheres Lighting Deep Learning Microscopy Optical Imaging
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