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Microscopy

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Fine-grained leukocyte classification with deep residual learning for microscopic images.

Computer methods and programs in biomedicine
BACKGROUND AND OBJECTIVE: Leukocyte classification and cytometry have wide applications in medical domain, previous researches usually exploit machine learning techniques to classify leukocytes automatically. However, constrained by the past developm...

A Fungus Spores Dataset and a Convolutional Neural Network Based Approach for Fungus Detection.

IEEE transactions on nanobioscience
Fungus is enormously notorious for food, human health, and archives. Fungus sign and symptoms in medical science are non-specific and asymmetrical for extremely large areas resulting into a challenging task of fungal detection. Various traditional an...

Neural network control of focal position during time-lapse microscopy of cells.

Scientific reports
Live-cell microscopy is quickly becoming an indispensable technique for studying the dynamics of cellular processes. Maintaining the specimen in focus during image acquisition is crucial for high-throughput applications, especially for long experimen...

MuDeRN: Multi-category classification of breast histopathological image using deep residual networks.

Artificial intelligence in medicine
MOTIVATION: Identifying carcinoma subtype can help to select appropriate treatment options and determining the subtype of benign lesions can be beneficial to estimate the patients' risk of developing cancer in the future. Pathologists' assessment of ...

Segmentation of corneal endothelium images using a U-Net-based convolutional neural network.

Artificial intelligence in medicine
Diagnostic information regarding the health status of the corneal endothelium may be obtained by analyzing the size and the shape of the endothelial cells in specular microscopy images. Prior to the analysis, the endothelial cells need to be extracte...

Deep learning massively accelerates super-resolution localization microscopy.

Nature biotechnology
The speed of super-resolution microscopy methods based on single-molecule localization, for example, PALM and STORM, is limited by the need to record many thousands of frames with a small number of observed molecules in each. Here, we present ANNA-PA...

Assessing microscope image focus quality with deep learning.

BMC bioinformatics
BACKGROUND: Large image datasets acquired on automated microscopes typically have some fraction of low quality, out-of-focus images, despite the use of hardware autofocus systems. Identification of these images using automated image analysis with hig...

Computational Sensing of Staphylococcus aureus on Contact Lenses Using 3D Imaging of Curved Surfaces and Machine Learning.

ACS nano
We present a cost-effective and portable platform based on contact lenses for noninvasively detecting Staphylococcus aureus, which is part of the human ocular microbiome and resides on the cornea and conjunctiva. Using S. aureus-specific antibodies a...

AxonDeepSeg: automatic axon and myelin segmentation from microscopy data using convolutional neural networks.

Scientific reports
Segmentation of axon and myelin from microscopy images of the nervous system provides useful quantitative information about the tissue microstructure, such as axon density and myelin thickness. This could be used for instance to document cell morphom...

Automated Interpretation of Blood Culture Gram Stains by Use of a Deep Convolutional Neural Network.

Journal of clinical microbiology
Microscopic interpretation of stained smears is one of the most operator-dependent and time-intensive activities in the clinical microbiology laboratory. Here, we investigated application of an automated image acquisition and convolutional neural net...