Literature DB >> 33937827

Construction of a Machine Learning Dataset through Collaboration: The RSNA 2019 Brain CT Hemorrhage Challenge.

Adam E Flanders1, Luciano M Prevedello1, George Shih1, Safwan S Halabi1, Jayashree Kalpathy-Cramer1, Robyn Ball1, John T Mongan1, Anouk Stein1, Felipe C Kitamura1, Matthew P Lungren1, Gagandeep Choudhary1, Lesley Cala1, Luiz Coelho1, Monique Mogensen1, Fanny Morón1, Elka Miller1, Ichiro Ikuta1, Vahe Zohrabian1, Olivia McDonnell1, Christie Lincoln1, Lubdha Shah1, David Joyner1, Amit Agarwal1, Ryan K Lee1, Jaya Nath1.   

Abstract

This dataset is composed of annotations of the five hemorrhage subtypes (subarachnoid, intraventricular, subdural, epidural, and intraparenchymal hemorrhage) typically encountered at brain CT. 2020 by the Radiological Society of North America, Inc.

Entities:  

Year:  2020        PMID： 33937827      PMCID： PMC8082297          DOI： 10.1148/ryai.2020190211

Source DB:  PubMed          Journal:  Radiol Artif Intell        ISSN： 2638-6100


  6 in total

1.  Deep learning algorithms for detection of critical findings in head CT scans: a retrospective study.

Authors:  Sasank Chilamkurthy; Rohit Ghosh; Swetha Tanamala; Mustafa Biviji; Norbert G Campeau; Vasantha Kumar Venugopal; Vidur Mahajan; Pooja Rao; Prashant Warier
Journal:  Lancet       Date:  2018-10-11       Impact factor: 79.321

Review 2.  Intracerebral haemorrhage: current approaches to acute management.

Authors:  Charlotte Cordonnier; Andrew Demchuk; Wendy Ziai; Craig S Anderson
Journal:  Lancet       Date:  2018-10-06       Impact factor: 79.321

3.  Detecting Intracranial Hemorrhage with Deep Learning.

Authors:  Arjun Majumdar; Laura Brattain; Brian Telfer; Chad Farris; Jonathan Scalera
Journal:  Annu Int Conf IEEE Eng Med Biol Soc       Date:  2018-07

4.  Analysis of head CT scans flagged by deep learning software for acute intracranial hemorrhage.

Authors:  Daniel T Ginat
Journal:  Neuroradiology       Date:  2019-12-11       Impact factor: 2.804

5.  Application of Deep Learning in Neuroradiology: Brain Haemorrhage Classification Using Transfer Learning.

Authors:  Awwal Muhammad Dawud; Kamil Yurtkan; Huseyin Oztoprak
Journal:  Comput Intell Neurosci       Date:  2019-06-03

6.  Expert-level detection of acute intracranial hemorrhage on head computed tomography using deep learning.

Authors:  Weicheng Kuo; Christian Hӓne; Pratik Mukherjee; Jitendra Malik; Esther L Yuh
Journal:  Proc Natl Acad Sci U S A       Date:  2019-10-21       Impact factor: 11.205
  6 in total
  16 in total

Review 1.  Systematic Review of Generative Adversarial Networks (GANs) for Medical Image Classification and Segmentation.

Authors:  Jiwoong J Jeong; Amara Tariq; Tobiloba Adejumo; Hari Trivedi; Judy W Gichoya; Imon Banerjee
Journal:  J Digit Imaging       Date:  2022-01-12       Impact factor: 4.056

2.  Foundations of Lesion Detection Using Machine Learning in Clinical Neuroimaging.

Authors:  Manoj Mannil; Nicolin Hainc; Risto Grkovski; Sebastian Winklhofer
Journal:  Acta Neurochir Suppl       Date:  2022

3.  GAN augmentation for multiclass image classification using hemorrhage detection as a case-study.

Authors:  Jiwoong Jason Jeong; Bhavik Patel; Imon Banerjee
Journal:  J Med Imaging (Bellingham)       Date:  2022-06-23

4.  Forging Connections in Latin America to Advance AI in Radiology.

Authors:  Felipe Campos Kitamura; Felipe Barjud Pereira do Nascimento; Guillermo Elizondo-Riojas; Hernán Chaves; Héctor Henríquez Leighton; Emmanuel Salinas-Miranda; Thiago Júlio; Antônio José da Rocha; César Higa Nomura
Journal:  Radiol Artif Intell       Date:  2022-08-31

5.  Moving from ImageNet to RadImageNet for Improved Transfer Learning and Generalizability.

Authors:  Alexandre Cadrin-Chênevert
Journal:  Radiol Artif Intell       Date:  2022-08-10

6.  Artificial Intelligence with Statistical Confidence Scores for Detection of Acute or Subacute Hemorrhage on Noncontrast CT Head Scans.

Authors:  Eli Gibson; Bogdan Georgescu; Pascal Ceccaldi; Pierre-Hugo Trigan; Youngjin Yoo; Jyotipriya Das; Thomas J Re; Vishwanath Rs; Abishek Balachandran; Eva Eibenberger; Andrei Chekkoury; Barbara Brehm; Uttam K Bodanapally; Savvas Nicolaou; Pina C Sanelli; Thomas J Schroeppel; Thomas Flohr; Dorin Comaniciu; Yvonne W Lui
Journal:  Radiol Artif Intell       Date:  2022-04-20

Review 7.  Artificial intelligence with deep learning in nuclear medicine and radiology.

Authors:  Milan Decuyper; Jens Maebe; Roel Van Holen; Stefaan Vandenberghe
Journal:  EJNMMI Phys       Date:  2021-12-11

8.  An optimal deep learning framework for multi-type hemorrhagic lesions detection and quantification in head CT images for traumatic brain injury.

Authors:  Aniwat Phaphuangwittayakul; Yi Guo; Fangli Ying; Ahmad Yahya Dawod; Salita Angkurawaranon; Chaisiri Angkurawaranon
Journal:  Appl Intell (Dordr)       Date:  2021-09-25       Impact factor: 5.019

Review 9.  Automated Detection and Screening of Traumatic Brain Injury (TBI) Using Computed Tomography Images: A Comprehensive Review and Future Perspectives.

Authors:  Vidhya V; Anjan Gudigar; U Raghavendra; Ajay Hegde; Girish R Menon; Filippo Molinari; Edward J Ciaccio; U Rajendra Acharya
Journal:  Int J Environ Res Public Health       Date:  2021-06-16       Impact factor: 3.390

10.  The Trials and Tribulations of Assembling Large Medical Imaging Datasets for Machine Learning Applications.

Authors:  Kirti Magudia; Christopher P Bridge; Katherine P Andriole; Michael H Rosenthal
Journal:  J Digit Imaging       Date:  2021-10-04       Impact factor: 4.903
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