scholarly journals Deep learning computer vision algorithm for detecting kidney stone composition

2020 ◽  
Vol 125 (6) ◽  
pp. 920-924 ◽  
Author(s):  
Kristian M. Black ◽  
Hei Law ◽  
Ali Aldoukhi ◽  
Jia Deng ◽  
Khurshid R. Ghani
Keyword(s):  
Computer Vision ◽  
Deep Learning ◽  
Kidney Stone ◽  
Stone Composition ◽  
Vision Algorithm

scholarly journals PD04-06 DEEP LEARNING COMPUTER VISION ALGORITHM FOR DETECTING KIDNEY STONE COMPOSITION: TOWARDS AN AUTOMATED FUTURE

2019 ◽  
Vol 201 (Supplement 4) ◽  
Author(s):  
Ali H Aldoukhi* ◽  
Hei Law ◽  
Kristian M Black ◽  
William W Roberts ◽  
Jia Deng ◽  
...  
Keyword(s):  
Computer Vision ◽  
Deep Learning ◽  
Kidney Stone ◽  
Stone Composition ◽  
Vision Algorithm

Deep learning computer vision algorithm for detecting kidney stone composition: Towards an automated future

2019 ◽  
Vol 18 (1) ◽  
pp. e853-e854 ◽  
Author(s):  
K.M. Black ◽  
H. Law ◽  
A.H. Aldoukhi ◽  
W.W. Roberts ◽  
J. Deng ◽  
...  
Keyword(s):  
Computer Vision ◽  
Deep Learning ◽  
Kidney Stone ◽  
Stone Composition ◽  
Vision Algorithm

scholarly journals Drone Detection Using Image Processing Based on Deep Learning

2021 ◽  
Vol 44 (4) ◽  
pp. 36-39
Author(s):  
Florin-Bogdan MARIN ◽  
Mihaela MARIN
Keyword(s):  
Image Processing ◽  
Computer Vision ◽  
Deep Learning ◽  
Experimental Research ◽  
Movement Patterns ◽  
Vision Algorithm

The objective of this experimental research is to identify solutions to detect drones using computer vision algorithm. Nowadays danger of drones operating near airports and other important sites is of utmost importance. The proposed techniques resolution pictures with a good rate of detection. The technique is using information concerning movement patterns of drones.

scholarly journals Deep learning reveals hidden variables underlying NF-κB activation in single cells

2019 ◽  
Author(s):  
Parthiv Patel ◽  
Nir Drayman ◽  
Ping Liu ◽  
Mustafa Bilgic ◽  
Savaş Tay
Keyword(s):  
Computer Vision ◽  
Deep Learning ◽  
Cell Activation ◽  
Single Cells ◽  
Hidden Variables ◽  
Mechanistic Modeling ◽  
Environmental Cues ◽  
Great Heterogeneity ◽  
Cytokine Stimulation ◽  
Vision Algorithm

Individual cells show great heterogeneity when responding to environmental cues. For example, under cytokine stimulation some cells activate immune signaling pathways while others completely ignore the signal. The underlying sources of cellular variability have been inaccessible due to the destructive nature of experiments. Here we apply deep learning, live-cell analysis, and mechanistic modeling to uncover hidden variables controlling NF-κB activation in single-cells. Our computer-vision algorithm accurately predicts cells that will respond to pro-inflammatory TNF stimulation and shows that single-cell activation is pre-determined by minute amounts of “leaky” nuclear NF-κB localization before stimulation. Theoretical analysis predicts and experiments confirm that the ratio of NF-κB to its inhibitor IκB determines the activation probability of a given cell. Our results demonstrate how computer vision can study living-cells without the use of destructive measurements and settles the question of whether heterogenous NF-κB activation is controlled by pre-existing deterministic variables or purely stochastic ones.

scholarly journals Tensor Methods in Computer Vision and Deep Learning

2021 ◽  
Vol 109 (5) ◽  
pp. 863-890
Author(s):  
Yannis Panagakis ◽  
Jean Kossaifi ◽  
Grigorios G. Chrysos ◽  
James Oldfield ◽  
Mihalis A. Nicolaou ◽  
...  
Keyword(s):  
Computer Vision ◽  
Deep Learning ◽  
Tensor Methods

scholarly journals Maize-IAS: a maize image analysis software using deep learning for high-throughput plant phenotyping

Plant Methods ◽  
2021 ◽  
Vol 17 (1) ◽  
Author(s):  
Shuo Zhou ◽  
Xiujuan Chai ◽  
Zixuan Yang ◽  
Hongwu Wang ◽  
Chenxue Yang ◽  
...  
Keyword(s):  
Computer Vision ◽  
Image Analysis ◽  
Deep Learning ◽  
High Throughput ◽  
Batch Processing ◽  
Plant Phenotyping ◽  
Plant Science ◽  
Analysis Software ◽  
Image Analysis Software ◽  
Maize Growth

Abstract Background Maize (Zea mays L.) is one of the most important food sources in the world and has been one of the main targets of plant genetics and phenotypic research for centuries. Observation and analysis of various morphological phenotypic traits during maize growth are essential for genetic and breeding study. The generally huge number of samples produce an enormous amount of high-resolution image data. While high throughput plant phenotyping platforms are increasingly used in maize breeding trials, there is a reasonable need for software tools that can automatically identify visual phenotypic features of maize plants and implement batch processing on image datasets. Results On the boundary between computer vision and plant science, we utilize advanced deep learning methods based on convolutional neural networks to empower the workflow of maize phenotyping analysis. This paper presents Maize-IAS (Maize Image Analysis Software), an integrated application supporting one-click analysis of maize phenotype, embedding multiple functions: (I) Projection, (II) Color Analysis, (III) Internode length, (IV) Height, (V) Stem Diameter and (VI) Leaves Counting. Taking the RGB image of maize as input, the software provides a user-friendly graphical interaction interface and rapid calculation of multiple important phenotypic characteristics, including leaf sheath points detection and leaves segmentation. In function Leaves Counting, the mean and standard deviation of difference between prediction and ground truth are 1.60 and 1.625. Conclusion The Maize-IAS is easy-to-use and demands neither professional knowledge of computer vision nor deep learning. All functions for batch processing are incorporated, enabling automated and labor-reduced tasks of recording, measurement and quantitative analysis of maize growth traits on a large dataset. We prove the efficiency and potential capability of our techniques and software to image-based plant research, which also demonstrates the feasibility and capability of AI technology implemented in agriculture and plant science.

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