Can deep adult lung segmentation models generalize to the pediatric population?-Reference-Cited by-同舟云学术

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Can deep adult lung segmentation models generalize to the pediatric population?

Published:2023-11 Issue: Volume:229 Page:120531
ISSN:0957-4174
Container-title:Expert Systems with Applications
language:en
Short-container-title:Expert Systems with Applications

Author:

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

Publisher

Elsevier BV

Subject

Artificial Intelligence,Computer Science Applications,General Engineering

Reference38 articles.

1. Statistics notes: How to obtain the P value from a confidence interval;Altman;BMJ (Online),2011

2. Interpretation of the paediatric chest X-ray;Arthur;Paediatric Respiratory Reviews,2000

3. An evaluation of performance measures for arterial brain vessel segmentation;Aydin;BMC Medical Imaging,2021

4. A review on lung boundary detection in chest X-rays;Candemir,2019

5. Candemir, S., Antani, S., Jaeger, S., Browning, R., & Thoma, G. (2015). Lung Boundary Detection in Pediatric Chest X-rays. Medical Imaging 2015: Pacs and Imaging Informatics: Next Generation and Innovations. https://doi.org/10.1117/12.2081060.

Cited by 2 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. Noise-induced modality-specific pretext learning for pediatric chest X-ray image classification;Frontiers in Artificial Intelligence;2024-09-05

2. A Study on Reducing Big Data Image Annotation Burden Through Iterative Expert-In-The-Loop Strategy;2023 IEEE International Conference on Bioinformatics and Biomedicine (BIBM);2023-12-05

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