Semantically redundant training data removal and deep model classification performance: A study with chest X-rays-Reference-Cited by-同舟云学术

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Semantically redundant training data removal and deep model classification performance: A study with chest X-rays

Published:2024-07 Issue: Volume:115 Page:102379
ISSN:0895-6111
Container-title:Computerized Medical Imaging and Graphics
language:en
Short-container-title:Computerized Medical Imaging and Graphics

Author:

Rajaraman Sivaramakrishnan,Zamzmi Ghada,Yang Feng,Liang Zhaohui,Xue Zhiyun,Antani Sameer^ORCID

Funder

Intramural Research Program

National Institutes of Health

National Library of Medicine

Publisher

Elsevier BV

Reference25 articles.

1. Random Search for Hyper-Parameter Optimization;Bergstra;J. Mach. Learn. Res.,2012

2. Birodkar, V., Mobahi, H., Bengio, S.Birodkar, V., Mobahi, H., Bengio, S., 2019. Semantic Redundancies in Image-Classification Datasets: The 10% You Don’t Need..

3. Assessment of data augmentation strategies toward performance improvement of abnormality classification in chest radiographs;Ganesan;: Proc. Annu. Int. Conf. IEEE Eng. Med. Biol. Soc., EMBS,2019

4. Artificial intelligence for the detection of COVID-19 pneumonia on chest CT using multinational datasets;Harmon;Nat. Commun.,2020

5. CheXpert: A large chest radiograph dataset with uncertainty labels and expert comparison. 33rd;Irvin;AAAI Conf. Artif. Intell. AAAI 2019, 31st Innov. Appl. Artif. Intell. Conf. IAAI 2019 9th AAAI Symp. Educ. Adv. Artif. Intell. EAAI 2019,2019

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