scholarly journals Fast automated analysis of strong gravitational lenses with convolutional neural networks

Nature ◽  
2017 ◽  
Vol 548 (7669) ◽  
pp. 555-557 ◽  
Author(s):  
Yashar D. Hezaveh ◽  
Laurence Perreault Levasseur ◽  
Philip J. Marshall
Keyword(s):  
Neural Networks ◽  
Convolutional Neural Networks ◽  
Automated Analysis ◽  
Gravitational Lenses

scholarly journals An Extended Catalog of Galaxy–Galaxy Strong Gravitational Lenses Discovered in DES Using Convolutional Neural Networks

2019 ◽  
Vol 243 (1) ◽  
pp. 17 ◽  
Author(s):  
C. Jacobs ◽  
T. Collett ◽  
K. Glazebrook ◽  
E. Buckley-Geer ◽  
H. T. Diehl ◽  
...  
Keyword(s):  
Neural Networks ◽  
Convolutional Neural Networks ◽  
Gravitational Lenses

scholarly journals Automated analysis of foraminifera fossil records by image classification using a convolutional neural network

2020 ◽  
Vol 39 (2) ◽  
pp. 183-202 ◽  
Author(s):  
Ross Marchant ◽  
Martin Tetard ◽  
Adnya Pratiwi ◽  
Michael Adebayo ◽  
Thibault de Garidel-Thoron
Keyword(s):  
Neural Networks ◽  
Convolutional Neural Networks ◽  
Automated Analysis ◽  
Warm Pool ◽  
Automated Classification ◽  
Deep Convolutional Neural Networks ◽  
The North ◽  
Manual Counting ◽  
Large Foraminifera ◽  
Fossil Records

Abstract. Manual identification of foraminiferal morphospecies or morphotypes under stereo microscopes is time consuming for micropalaeontologists and not possible for nonspecialists. Therefore, a long-term goal has been to automate this process to improve its efficiency and repeatability. Recent advances in computation hardware have seen deep convolutional neural networks emerge as the state-of-the-art technique for image-based automated classification. Here, we describe a method for classifying large foraminifera image sets using convolutional neural networks. Construction of the classifier is demonstrated on the publicly available Endless Forams image set with a best accuracy of approximately 90 %. A complete automatic analysis is performed for benthic species dated to the last deglacial period for a sediment core from the north-eastern Pacific and for planktonic species dated from the present until 180 000 years ago in a core from the western Pacific warm pool. The relative abundances from automatic counting based on more than 500 000 images compare favourably with manual counting, showing the same signal dynamics. Our workflow opens the way to automated palaeoceanographic reconstruction based on computer image analysis and is freely available for use.

scholarly journals Quantitation of Suicidal Netosis Using Convolutional Neural Networks in Human Neutrophils

Blood ◽  
2016 ◽  
Vol 128 (22) ◽  
pp. 3686-3686
Author(s):  
Laila Elsherif ◽  
Carrington A. Metts ◽  
Noah Sciaky ◽  
Joshua N. Cooper ◽  
Stephen P. Holly ◽  
...  
Keyword(s):  
Neural Networks ◽  
Convolutional Neural Networks ◽  
Quantitative Methods ◽  
Conflicts Of Interest ◽  
Automated Analysis ◽  
Data Interpretation ◽  
Staining Intensity ◽  
Culture Conditions ◽  
Human Neutrophils ◽  
Vein Thrombosis

Abstract Suicidal NETosis (Neutrophil Extracellular Traps) is a mode of cell death involving de-condensation of chromatin and fusion with cytoplasmic granule proteins to create a net-like structure for entrapping pathogenic elements. Despite its name, NETosis does not occur exclusively in neutrophils but rather in all myeloid cells of the immune system except dendritic cells. A growing list of diseases and conditions implicate NETs in their etiology and the progression of symptoms. These conditions include sepsis, cystic fibrosis, lupus nephritis, acute respiratory distress syndrome (ARDS) and deep vein thrombosis. There are several different quantitative methods to assess NETosis making comparative analysis across the field of NETosis research extremely challenging. To further define NETosis and facilitate new discoveries in this field, we have developed a novel assay that combines high-content microscopy with Convolutional Neural Networks (CNN). CNN is the revolutionary image recognition technology used in popular social media facial features recognition systems to 1) quantify the percentage of NETotic and non-NETotic nuclei in a cell population, 2) classify NETotic, necrotic and normal nuclei in a mixed population of neutrophils and 3) differentiate between two different NETotic nuclei generated by two of the most commonly used agonists in NETosis research, the PKC activator PMA and A23187, a Ca+2 ionophore. Furthermore we have optimized neutrophil isolation and culture conditions to yield reproducible results with low inter-and intra-assay variability. Image acquisition was performed on adherent neutrophils using the automated BD Pathway Bio-imaging Systems with autofocusing function that allows imaging of approximately 10% of a well in a 96-well plate. An algorithm was created for image segmentation and recognition of NETotic nuclei based on size, DNA staining intensity and pattern. Data generated by this semi-automated analysis system were used to train a CNN to count NETotic and non-NETotic nuclei. These methods allowed us to calculate the EC50 for PMA (2.2 nM) and A23187 (2.5 uM) and to differentiate between their distinct mechanisms of NETosis. CNNs are able to classify NETotic phenotypes, without the use of expensive antibodies or reagents. Our assay represents a crucial first step for standardizing and quantitating NETosis, which will allow better data interpretation across different labs and provide a potential screening tool for known and as yet undiscovered NETosis agonists. Disclosures No relevant conflicts of interest to declare.

scholarly journals Finding strong gravitational lenses in the Kilo Degree Survey with Convolutional Neural Networks

2017 ◽  
Vol 472 (1) ◽  
pp. 1129-1150 ◽  
Author(s):  
C. E. Petrillo ◽  
C. Tortora ◽  
S. Chatterjee ◽  
G. Vernardos ◽  
L. V. E. Koopmans ◽  
...  
Keyword(s):  
Neural Networks ◽  
Convolutional Neural Networks ◽  
Gravitational Lenses
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