AI Medical Compendium Topic

Explore the latest research on artificial intelligence and machine learning in medicine.

Cell Count

Showing 21 to 30 of 64 articles

Clear Filters

Object detection for automatic cancer cell counting in zebrafish xenografts.

PloS one
Cell counting is a frequent task in medical research studies. However, it is often performed manually; thus, it is time-consuming and prone to human error. Even so, cell counting automation can be challenging to achieve, especially when dealing with ...

Deep neural net tracking of human pluripotent stem cells reveals intrinsic behaviors directing morphogenesis.

Stem cell reports
Lineage tracing is a powerful tool in developmental biology to interrogate the evolution of tissue formation, but the dense, three-dimensional nature of tissue limits the assembly of individual cell trajectories into complete reconstructions of devel...

Comparison of machine learning methods to predict udder health status based on somatic cell counts in dairy cows.

Scientific reports
Bovine mastitis is one of the most important economic and health issues in dairy farms. Data collection during routine recording procedures and access to large datasets have shed the light on the possibility to use trained machine learning algorithms...

Multi-model CNN fusion for sperm morphology analysis.

Computers in biology and medicine
Infertility is a common disorder affecting 20% of couples worldwide. Furthermore, 40% of all cases are related to male infertility. The first step in the determination of male infertility is semen analysis. The morphology, concentration, and motility...

Automating cell counting in fluorescent microscopy through deep learning with c-ResUnet.

Scientific reports
Counting cells in fluorescent microscopy is a tedious, time-consuming task that researchers have to accomplish to assess the effects of different experimental conditions on biological structures of interest. Although such objects are generally easy t...

High-Throughput Recognition of Tumor Cells Using Label-Free Elemental Characteristics Based on Interpretable Deep Learning.

Analytical chemistry
With cancer seriously hampering the increasing life expectancy of people, developing an instant diagnostic method has become an urgent objective. In this work, we developed a label-free laser-induced breakdown spectroscopy (LIBS) method for high-thro...

Identifying tumor cells at the single-cell level using machine learning.

Genome biology
Tumors are complex tissues of cancerous cells surrounded by a heterogeneous cellular microenvironment with which they interact. Single-cell sequencing enables molecular characterization of single cells within the tumor. However, cell annotation-the a...

A disector-based framework for the automatic optical fractionator.

Journal of chemical neuroanatomy
Stereology-based methods provide the current state-of-the-art approaches for accurate quantification of numbers and other morphometric parameters of biological objects in stained tissue sections. The advent of artificial intelligence (AI)-based deep ...