Literature DB >> 31120793

Tackling the Radiological Society of North America Pneumonia Detection Challenge.

Ian Pan1,2, Alexandre Cadrin-Chênevert3,4, Phillip M Cheng5.   

Abstract

OBJECTIVE. We provide overviews of deep learning approaches used by two top-placing teams for the 2018 Radiological Society of North America (RSNA) Pneumonia Detection Challenge. CONCLUSION. Practical applications of deep learning techniques, as well as insights into the annotation of the data, were keys to success in accurately detecting pneumonia on chest radiographs for the competition.

Entities:  

Keywords:  artificial intelligence; convolutional neural network; deep learning; detection; pneumonia

Year:  2019        PMID： 31120793     DOI： 10.2214/AJR.19.21512

Source DB:  PubMed          Journal:  AJR Am J Roentgenol        ISSN： 0361-803X            Impact factor:   3.959


  11 in total

1.  Clinical Explainability Failure (CEF) & Explainability Failure Ratio (EFR) - Changing the Way We Validate Classification Algorithms.

Authors:  Vasantha Kumar Venugopal; Rohit Takhar; Salil Gupta; Vidur Mahajan
Journal:  J Med Syst       Date:  2022-03-05       Impact factor: 4.460

2.  External Validation of Deep Learning Algorithms for Radiologic Diagnosis: A Systematic Review.

Authors:  Alice C Yu; Bahram Mohajer; John Eng
Journal:  Radiol Artif Intell       Date:  2022-05-04

3.  Comparison of Chest Radiograph Interpretations by Artificial Intelligence Algorithm vs Radiology Residents.

Authors:  Joy T Wu; Ken C L Wong; Yaniv Gur; Nadeem Ansari; Alexandros Karargyris; Arjun Sharma; Michael Morris; Babak Saboury; Hassan Ahmad; Orest Boyko; Ali Syed; Ashutosh Jadhav; Hongzhi Wang; Anup Pillai; Satyananda Kashyap; Mehdi Moradi; Tanveer Syeda-Mahmood
Journal:  JAMA Netw Open       Date:  2020-10-01

4.  Identification of Images of COVID-19 from Chest X-rays Using Deep Learning: Comparing COGNEX VisionPro Deep Learning 1.0™ Software with Open Source Convolutional Neural Networks.

Authors:  Arjun Sarkar; Joerg Vandenhirtz; Jozsef Nagy; David Bacsa; Mitchell Riley
Journal:  SN Comput Sci       Date:  2021-03-10

5.  INASNET: Automatic identification of coronavirus disease (COVID-19) based on chest X-ray using deep neural network.

Authors:  Murukessan Perumal; Akshay Nayak; R Praneetha Sree; M Srinivas
Journal:  ISA Trans       Date:  2022-03-03       Impact factor: 5.911

6.  COVID-WideNet-A capsule network for COVID-19 detection.

Authors:  P K Gupta; Mohammad Khubeb Siddiqui; Xiaodi Huang; Ruben Morales-Menendez; Harsh Pawar; Hugo Terashima-Marin; Mohammad Saif Wajid
Journal:  Appl Soft Comput       Date:  2022-03-29       Impact factor: 8.263

Review 7.  Pediatric chest radiograph interpretation: how far has artificial intelligence come? A systematic literature review.

Authors:  Sirwa Padash; Mohammad Reza Mohebbian; Scott J Adams; Robert D E Henderson; Paul Babyn
Journal:  Pediatr Radiol       Date:  2022-04-23

8.  Lung nodule detection in chest X-rays using synthetic ground-truth data comparing CNN-based diagnosis to human performance.

Authors:  Manuel Schultheiss; Philipp Schmette; Jannis Bodden; Juliane Aichele; Christina Müller-Leisse; Felix G Gassert; Florian T Gassert; Joshua F Gawlitza; Felix C Hofmann; Daniel Sasse; Claudio E von Schacky; Sebastian Ziegelmayer; Fabio De Marco; Bernhard Renger; Marcus R Makowski; Franz Pfeiffer; Daniela Pfeiffer
Journal:  Sci Rep       Date:  2021-08-04       Impact factor: 4.379

9.  Deep Learning-Based Four-Region Lung Segmentation in Chest Radiography for COVID-19 Diagnosis.

Authors:  Young-Gon Kim; Kyungsang Kim; Dufan Wu; Hui Ren; Won Young Tak; Soo Young Park; Yu Rim Lee; Min Kyu Kang; Jung Gil Park; Byung Seok Kim; Woo Jin Chung; Mannudeep K Kalra; Quanzheng Li
Journal:  Diagnostics (Basel)       Date:  2022-01-03

10.  Industry 4.0 technologies and their applications in fighting COVID-19 pandemic using deep learning techniques.

Authors:  Muhammad Ahmad; Saima Sadiq; Ala' Abdulmajid Eshmawi; Ala Saleh Alluhaidan; Muhammad Umer; Saleem Ullah; Michele Nappi
Journal:  Comput Biol Med       Date:  2022-03-21       Impact factor: 6.698
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