scholarly journals High throughput assessment of cells and tissues: Bayesian classification of spectral metrics from infrared vibrational spectroscopic imaging data

2006 ◽  
Vol 1758 (7) ◽  
pp. 830-845 ◽  
Author(s):  
Rohit Bhargava ◽  
Daniel C. Fernandez ◽  
Stephen M. Hewitt ◽  
Ira W. Levin
Keyword(s):  
High Throughput ◽  
Spectroscopic Imaging ◽  
Bayesian Classification ◽  
Imaging Data

Nearest Neighbor-Based Strategy to Optimize Multi-View Triplet Network for Classification of Small-Sample Medical Imaging Data

2021 ◽  
pp. 1-15
Author(s):  
Phawis Thammasorn ◽  
Wanpracha A. Chaovalitwongse ◽  
Daniel S. Hippe ◽  
Landon S. Wootton ◽  
Eric C. Ford ◽  
...  
Keyword(s):  
Medical Imaging ◽  
Nearest Neighbor ◽  
Small Sample ◽  
Imaging Data ◽  
Medical Imaging Data

scholarly journals Convolutional Neural Network-Based Artificial Intelligence for Classification of Protein Localization Patterns

Biomolecules ◽  
2021 ◽  
Vol 11 (2) ◽  
pp. 264
Author(s):  
Kaisa Liimatainen ◽  
Riku Huttunen ◽  
Leena Latonen ◽  
Pekka Ruusuvuori
Keyword(s):  
Neural Network ◽  
Artificial Intelligence ◽  
Convolutional Neural Network ◽  
High Throughput ◽  
Protein Function ◽  
Protein Localization ◽  
Convolutional Network ◽  
High Throughput Analysis ◽  
Fully Convolutional Network

Identifying localization of proteins and their specific subpopulations associated with certain cellular compartments is crucial for understanding protein function and interactions with other macromolecules. Fluorescence microscopy is a powerful method to assess protein localizations, with increasing demand of automated high throughput analysis methods to supplement the technical advancements in high throughput imaging. Here, we study the applicability of deep neural network-based artificial intelligence in classification of protein localization in 13 cellular subcompartments. We use deep learning-based on convolutional neural network and fully convolutional network with similar architectures for the classification task, aiming at achieving accurate classification, but importantly, also comparison of the networks. Our results show that both types of convolutional neural networks perform well in protein localization classification tasks for major cellular organelles. Yet, in this study, the fully convolutional network outperforms the convolutional neural network in classification of images with multiple simultaneous protein localizations. We find that the fully convolutional network, using output visualizing the identified localizations, is a very useful tool for systematic protein localization assessment.

scholarly journals Analysis of Variance in Spectroscopic Imaging Data from Human Tissues

2011 ◽  
Vol 84 (2) ◽  
pp. 1063-1069 ◽  
Author(s):  
Jin Tae Kwak ◽  
Rohith Reddy ◽  
Saurabh Sinha ◽  
Rohit Bhargava
Keyword(s):  
Analysis Of Variance ◽  
Spectroscopic Imaging ◽  
Human Tissues ◽  
Imaging Data

Learning from Enhanced Contextual Similarity in Brain Imaging Data for Classification of Schizophrenia

2017 ◽  
pp. 265-275 ◽  
Author(s):  
Tewodros Mulugeta Dagnew ◽  
Letizia Squarcina ◽  
Massimo W. Rivolta ◽  
Paolo Brambilla ◽  
Roberto Sassi
Keyword(s):  
Brain Imaging ◽  
Imaging Data ◽  
Brain Imaging Data
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