![]() Reagent-free and rapid assessment of T cell activation state using diffraction phase microscopy and deep learning. Label-free high-throughput leukemia detection by holographic microscopy. Real-time stain-free classification of cancer cells and blood cells using interferometric phase microscopy and machine learning. Deep-learning-assisted biophysical imaging cytometry at massive throughput delineates cell population heterogeneity. Classification of human white blood cells using machine learning for stain-free imaging flow cytometry. Label-free identification of white blood cells using machine learning. Deep learning for cellular image analysis. Methods for segmentation and classification of digital microscopy tissue images. Virtual-freezing fluorescence imaging flow cytometry. Intelligent image-activated cell sorting. 2016 IEEE Conference on Computer Vision and Pattern Recognition 770–778 (IEEE, 2016). Deep residual learning for image recognition. Label-free leukemia monitoring by computer vision. 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) 1–9 (IEEE, 2015).ĭoan, M. An open-source solution for advanced imaging flow cytometry data analysis using machine learning. Objective assessment of stored blood quality by deep learning. ![]() Dimensionality reduction for visualizing single-cell data using UMAP. Diffusion maps for high-dimensional single-cell analysis of differentiation data. Reconstructing cell cycle and disease progression using deep learning. Label-free cell cycle analysis for high-throughput imaging flow cytometry. Cell Profiler Analyst: data exploration and analysis software for complex image-based screens. ![]() High-throughput cell imaging and classification by narrowband and low-spectral-resolution Raman microscopy. Quantitative phase imaging flow cytometry for ultra-large-scale single-cell biophysical phenotyping. Data-analysis strategies for image-based cell profiling. Machine learning in cell biology-teaching computers to recognize phenotypes. Single-cell analysis tools for drug discovery and development. Deep learning with microfluidics for biotechnology. An imaging flow cytometric method for measuring cell division history and molecular symmetry during mitosis. Simultaneous assessment of autophagy and apoptosis using multispectral imaging cytometry. A method for evaluating the use of fluorescent dyes to track proliferation in cell lines by dye dilution. Multispectral imaging flow cytometry reveals distinct frequencies of γ-H2AX foci induction in DNA double strand break repair defective human cell lines. Quantifying nuclear p65 as a parameter for NF-κB activation: correlation between ImageStream cytometry, microscopy, and Western blot. Maguire, O., Collins, C., O’Loughlin, K., Miecznikowski, H. Impaired expression and function of toll-like receptor 7 in hepatitis C virus infection in human hepatoma cells. Cellular image analysis and imaging by flow cytometry. The protocol requires 40 h when training from scratch and 1 h when using a pre-trained model.īasiji, D. The protocol enables a flexible and friendly environment for morphological phenotyping using supervised and weakly supervised learning and the subsequent exploration of the deep learning features using multi-dimensional visualization tools. All steps of the protocol are provided as open-source Python as well as MATLAB runtime scripts, through both command-line and graphic user interfaces. We describe how to acquire and transform suitable input data as well as the steps required for deep learning training and inference using an open-source web-based application. This protocol describes the application of deep learning to single-cell images to perform supervised cell classification and weakly supervised learning, using example data from an experiment exploring red blood cell morphology. Deep learning offers the potential to extract more than meets the eye from images captured by imaging flow cytometry.
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