Deep learning (DL) is a subfield of machine learning (ML), which has recently demonstrated its potency to significantly improve the quantification and classification workflows in biomedical and clinical applications. Among the end applications profoundly benefitting from DL, cellular morphology quantification is one of the pioneers. Here, we first briefly explain fundamental concepts in DL and then we review some of the emerging DL-enabled applications in cell morphology quantification in the fields of embryology, point-of-care ovulation testing, as a predictive tool for fetal heart pregnancy, cancer diagnostics via classification of cancer histology images, autosomal polycystic kidney disease, and chronic kidney diseases
For a century, the nucleus has been the focus of extensive investigations in cell biology. However, ...
The computer-assisted analysis for better interpreting images have been longstanding issues in the m...
The computer-aided analysis in the medical imaging field has attracted a lot of attention for the pa...
The aim of this workflow is to quantify the morphology of pancreatic stem cells lying on a 2D polyst...
Classification of cancer cellularity within tissue samples is currently a manual process performed b...
Background: Deep learning (DL) is a representation learning approach ideally suited for image analys...
This study applied a deep-learning cell identification algorithm to diagnostic images from the colon...
This study applied a deep-learning cell identification algorithm to diagnostic images from the colon...
Deep learning is a subcategory of machine learning and artificial intelligence. Instead of using exp...
Recent advances in computer vision and machine learning underpin a collection of algorithms with an ...
We show that deep convolutional neural networks combined with nonlinear dimension reduction enable r...
Deep learning offers the potential to extract more than meets the eye from images captured by imagin...
The interest in Deep Learning (DL) has seen an exponential growth in the last ten years, producing a...
Analysis of biomedical images requires computational expertize that are uncommon among biomedical sc...
Image cytometry is the analysis of cell properties from microscopy image data and is used ubiquitous...
For a century, the nucleus has been the focus of extensive investigations in cell biology. However, ...
The computer-assisted analysis for better interpreting images have been longstanding issues in the m...
The computer-aided analysis in the medical imaging field has attracted a lot of attention for the pa...
The aim of this workflow is to quantify the morphology of pancreatic stem cells lying on a 2D polyst...
Classification of cancer cellularity within tissue samples is currently a manual process performed b...
Background: Deep learning (DL) is a representation learning approach ideally suited for image analys...
This study applied a deep-learning cell identification algorithm to diagnostic images from the colon...
This study applied a deep-learning cell identification algorithm to diagnostic images from the colon...
Deep learning is a subcategory of machine learning and artificial intelligence. Instead of using exp...
Recent advances in computer vision and machine learning underpin a collection of algorithms with an ...
We show that deep convolutional neural networks combined with nonlinear dimension reduction enable r...
Deep learning offers the potential to extract more than meets the eye from images captured by imagin...
The interest in Deep Learning (DL) has seen an exponential growth in the last ten years, producing a...
Analysis of biomedical images requires computational expertize that are uncommon among biomedical sc...
Image cytometry is the analysis of cell properties from microscopy image data and is used ubiquitous...
For a century, the nucleus has been the focus of extensive investigations in cell biology. However, ...
The computer-assisted analysis for better interpreting images have been longstanding issues in the m...
The computer-aided analysis in the medical imaging field has attracted a lot of attention for the pa...