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Microscopy

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Characterization of an artificial intelligence model for ranking static images of blastocyst stage embryos.

Fertility and sterility
OBJECTIVE: To perform a series of analyses characterizing an artificial intelligence (AI) model for ranking blastocyst-stage embryos. The primary objective was to evaluate the benefit of the model for predicting clinical pregnancy, whereas the second...

Unsupervised discovery of dynamic cell phenotypic states from transmitted light movies.

PLoS computational biology
Identification of cell phenotypic states within heterogeneous populations, along with elucidation of their switching dynamics, is a central challenge in modern biology. Conventional single-cell analysis methods typically provide only indirect, static...

Deep learning identifies inflamed fat as a risk factor for lymph node metastasis in early colorectal cancer.

The Journal of pathology
The spread of early-stage (T1 and T2) adenocarcinomas to locoregional lymph nodes is a key event in disease progression of colorectal cancer (CRC). The cellular mechanisms behind this event are not completely understood and existing predictive biomar...

Assessing red blood cell deformability from microscopy images using deep learning.

Lab on a chip
Red blood cells (RBCs) must be highly deformable to transit through the microvasculature to deliver oxygen to tissues. The loss of RBC deformability resulting from pathology, natural aging, or storage in blood bags can impede the proper function of t...

[EMPAIA-ecosystem for pathology diagnostics with AI assistance].

Der Pathologe
Applications of deep learning and other artificial intelligence techniques play an increasing role in pathological research. In contrast to research, applications in clinical routine are rare so far, although the first certified solutions have alread...

Mitotic Index Determination on Live Cells From Label-Free Acquired Quantitative Phase Images Using a Supervised Autoencoder.

IEEE/ACM transactions on computational biology and bioinformatics
This interdisciplinary work focuses on the interest of a new auto-encoder for supervised classification of live cell populations growing in a thermostated imaging station and acquired by a Quantitative Phase Imaging (QPI) camera. This type of camera ...

Using Convolutional Neural Networks to Measure the Physiological Age of Caenorhabditis elegans.

IEEE/ACM transactions on computational biology and bioinformatics
Caenorhabditis elegans (C. elegans) is a popular and excellent model for studies of aging due to its short lifespan. Methods for precisely measuring the physiological age of C. elegans are critically needed, especially for antiaging drug screening an...

Physics-based learning with channel attention for Fourier ptychographic microscopy.

Journal of biophotonics
Fourier ptychographic microscopy (FPM) is a computational imaging technology for large field-of-view, high resolution and quantitative phase imaging. In FPM, low-resolution intensity images captured with angle-varying illumination are synthesized in ...

Reduced and stable feature sets selection with random forest for neurons segmentation in histological images of macaque brain.

Scientific reports
In preclinical research, histology images are produced using powerful optical microscopes to digitize entire sections at cell scale. Quantification of stained tissue relies on machine learning driven segmentation. However, such methods require multip...

WBC-based segmentation and classification on microscopic images: a minor improvement.

F1000Research
Introduction White blood cells (WBCs) are immunity cells which fight against viruses and bacteria in the human body. Microscope images of captured WBCs for processing and analysis are important to interpret the body condition. At present, there is no...