Role of Big Data in Medical Imaging Modalities to Extract the Hidden Patterns using HIPI in HDFS Environment

2019 ◽  
Author(s):  
Yogesh Kumar Gupta
Keyword(s):  
Big Data ◽  
Medical Imaging ◽  
Imaging Modalities ◽  
Hidden Patterns

scholarly journals On the Role of Artificial Intelligence in Medical Imaging of COVID-19

2020 ◽  
Author(s):  
Jannis Born ◽  
David Beymer ◽  
Deepta Rajan ◽  
Adam Coy ◽  
Vandana V. Mukherjee ◽  
...  
Keyword(s):  
Medical Imaging ◽  
Large Scale ◽  
Meta Analysis ◽  
Imaging Modalities ◽  
Clinical Implications ◽  
Review Of The Literature ◽  
Dataset Size ◽  
Coronavirus Infection ◽  
Keyword Searches

AbstractPurposeThe global COVID-19 pandemic has accelerated the development of numerous digital technologies in medicine from telemedicine to remote monitoring. Concurrently, the pandemic has resulted in huge pressures on healthcare systems. Medical imaging from chest radiographs to computed tomography and ultrasound of the thorax have played an important role in the diagnosis and management of the coronavirus infection.MethodsWe undertook a systematic review of the literature focused on MI in COVID-19 and the utility of AI. Keyword searches were performed on PubMed and preprint servers including arXiv, bioRxiv and medRxiv; 338 papers were included in a meta-analysis and manually reviewed to assess solutions in AI according to their clinical relevance. The maturity of the papers was evaluated based on four criteria: peer-review, patient dataset size, algorithmic complexity and usage of the AI in clinical practice.ResultsIn the first three quarters of 2020, we identified 3444 papers on MI in COVID-19, of which 556 had at least some focus on AI. 2039 of 3444 were specific to imaging modalities and predominantly (80.7%) focused on CT (9.9% on LUS and 9.5% on CXR). The AI literature was predominantly focused on CXR (51.2%), 36.1% on CT and 1.8% on LUS. Only a small portion of the papers were judged as mature (3.8%) and most AI papers focused on disease detection (72.8%).ConclusionsThis review evidences a disparity between clinicians and the AI community, both in the focus on imaging modalities and performed tasks. Better collaboration is needed to allocate resources optimally for the development of clinically relevant solutions that are validated on large-scale patient data.Clinical implicationsAI may aid clinicians and radiologists by providing better tools for localization and quantification of disease features and changes thereof, and, with integration of clinical data, may provide better diagnostic performance and prognostic value.

scholarly journals Role of Big Data in Business and Information Technology

2018 ◽  
Vol 3 (4) ◽  
pp. 32
Author(s):  
Deepali Deoram Patil
Keyword(s):  
Decision Making ◽  
Information Technology ◽  
Big Data ◽  
Unstructured Data ◽  
Review Of Literature ◽  
Tremendous Amount ◽  
Hidden Patterns ◽  
Value Of It ◽  
The Impact

This paper aims to evaluate the significance of the impact of big data in various fields and determine its future in those areas. A comprehensive review of literature on influence and trends of big data variables with respect to different fields built hypothetical foundation of the paper. The big data is a tremendous amount of structured and unstructured data that provides major insights into a particular field which further supports the decision-making systems and builds the foundation of company’s competitiveness. Data contributes in the processing, evaluation, understanding and decision-making steps. Big data influences every area that constitutes of pools of data. This proposed paper is beneficial to accumulate the knowledge regarding the hidden patterns and insights of big data variables and assess the value of it in major areas such as business and information technology.

THE ROLE OF SUPERCONDUCTING MAGNETS IN NMR MEDICAL IMAGING

1984 ◽  
Vol 45 (C1) ◽  
pp. C1-685-C1-690
Author(s):  
M. A. Green ◽  
J. R. Singer
Keyword(s):  
Medical Imaging ◽  
Superconducting Magnets

LNTP-MDBN: Big Data Integrated Learning Framework for Heterogeneous Image Set Classification

2019 ◽  
Vol 15 (2) ◽  
pp. 227-236
Author(s):  
D. Franklin Vinod ◽  
V. Vasudevan
Keyword(s):  
Big Data ◽  
Medical Image ◽  
Research Study ◽  
Big Data Analytics ◽  
Learning Framework ◽  
Image Set Classification ◽  
Filtering Technique ◽  
Image Set ◽  
Hidden Patterns ◽  
Global Data

Background: With the explosive growth of global data, the term Big Data describes the enormous size of dataset through the detailed analysis. The big data analytics revealed the hidden patterns and secret correlations among the values. The major challenges in Big data analysis are due to increase of volume, variety, and velocity. The capturing of images with multi-directional views initiates the image set classification which is an attractive research study in the volumetricbased medical image processing. Methods: This paper proposes the Local N-ary Ternary Patterns (LNTP) and Modified Deep Belief Network (MDBN) to alleviate the dimensionality and robustness issues. Initially, the proposed LNTP-MDBN utilizes the filtering technique to identify and remove the dependent and independent noise from the images. Then, the application of smoothening and the normalization techniques on the filtered image improves the intensity of the images. Results: The LNTP-based feature extraction categorizes the heterogeneous images into different categories and extracts the features from each category. Based on the extracted features, the modified DBN classifies the normal and abnormal categories in the image set finally. Conclusion: The comparative analysis of proposed LNTP-MDBN with the existing pattern extraction and DBN learning models regarding classification accuracy and runtime confirms the effectiveness in mining applications.

scholarly journals Structural and Functional Imaging in Glioma Management

Neurosurgery ◽  
2020 ◽  
Author(s):  
Bledi C Brahimaj ◽  
Ryan B Kochanski ◽  
John J Pearce ◽  
Melike Guryildirim ◽  
Carter S Gerard ◽  
...  
Keyword(s):  
Diffusion Tensor ◽  
Imaging Modalities ◽  
Hemispheric Dominance ◽  
Tumor Control ◽  
Histopathological Diagnosis ◽  
Glioma Surgery ◽  
Multiple Modalities ◽  
Advantages And Disadvantages ◽  
Multiple Imaging

Abstract The goal of glioma surgery is maximal safe resection in order to provide optimal tumor control and survival benefit to the patient. There are multiple imaging modalities beyond traditional contrast-enhanced magnetic resonance imaging (MRI) that have been incorporated into the preoperative workup of patients presenting with gliomas. The aim of these imaging modalities is to identify cortical and subcortical areas of eloquence, and their relationship to the lesion. In this article, multiple modalities are described with an emphasis on the underlying technology, clinical utilization, advantages, and disadvantages of each. functional MRI and its role in identifying hemispheric dominance and areas of language and motor are discussed. The nuances of magnetoencephalography and transcranial magnetic stimulation in localization of eloquent cortex are examined, as well as the role of diffusion tensor imaging in defining normal white matter tracts in glioma surgery. Lastly, we highlight the role of stimulated Raman spectroscopy in intraoperative histopathological diagnosis of tissue to guide tumor resection. Tumors may shift the normal arrangement of functional anatomy in the brain; thus, utilization of multiple modalities may be helpful in operative planning and patient counseling for successful surgery.

scholarly journals Role of different imaging modalities in the evaluation of normal and diseased thymus

2020 ◽  
Vol 14 (1) ◽  
Author(s):  
S. A. Hussein ◽  
Y. Y. Sabri ◽  
M. A. Fouad ◽  
H. H. Al-Zawam ◽  
N. M. Mohamed
Keyword(s):  
Imaging Modalities

scholarly journals Out of the loop? On the radical and the routine in urban big data

Urban Studies ◽  
2021 ◽  
pp. 004209802110140
Author(s):  
Sarah Barns
Keyword(s):  
Artificial Intelligence ◽  
Big Data ◽  
Computational Models ◽  
Data Driven ◽  
Urban Systems ◽  
Out Of The Loop ◽  
Urban Futures ◽  
Urban Behaviour ◽  
The City

This commentary interrogates what it means for routine urban behaviours to now be replicating themselves computationally. The emergence of autonomous or artificial intelligence points to the powerful role of big data in the city, as increasingly powerful computational models are now capable of replicating and reproducing existing spatial patterns and activities. I discuss these emergent urban systems of learned or trained intelligence as being at once radical and routine. Just as the material and behavioural conditions that give rise to urban big data demand attention, so do the generative design principles of data-driven models of urban behaviour, as they are increasingly put to use in the production of replicable, autonomous urban futures.

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