BPG is committed to discovery and dissemination of knowledge
Cited by in F6Publishing
For: Çallı E, Sogancioglu E, van Ginneken B, van Leeuwen KG, Murphy K. Deep learning for chest X-ray analysis: A survey. Med Image Anal 2021;72:102125. [PMID: 34171622 DOI: 10.1016/j.media.2021.102125] [Cited by in Crossref: 3] [Cited by in F6Publishing: 1] [Article Influence: 3.0] [Reference Citation Analysis]
Number Citing Articles
1 You A, Kim JK, Ryu IH, Yoo TK. Application of generative adversarial networks (GAN) for ophthalmology image domains: a survey. Eye Vis (Lond) 2022;9:6. [PMID: 35109930 DOI: 10.1186/s40662-022-00277-3] [Cited by in Crossref: 2] [Cited by in F6Publishing: 2] [Article Influence: 2.0] [Reference Citation Analysis]
2 Ziegler J, Pfitzner B, Schulz H, Saalbach A, Arnrich B. Defending against Reconstruction Attacks through Differentially Private Federated Learning for Classification of Heterogeneous Chest X-ray Data. Sensors (Basel) 2022;22:5195. [PMID: 35890875 DOI: 10.3390/s22145195] [Reference Citation Analysis]
3 Gu F, Wu X, Wu W, Wang Z, Yang X, Chen Z, Wang Z, Chen G. Performance of deep learning in the detection of intracranial aneurysm: a systematic review and meta-analysis. European Journal of Radiology 2022. [DOI: 10.1016/j.ejrad.2022.110457] [Reference Citation Analysis]
4 Raghu VK, Lu MT. Can deep learning classify stroke subtypes from chest X-rays? EBioMedicine 2021;70:103517. [PMID: 34364166 DOI: 10.1016/j.ebiom.2021.103517] [Reference Citation Analysis]
5 Roth A, Wüstefeld K, Weichert F. A Data-Centric Augmentation Approach for Disturbed Sensor Image Segmentation. J Imaging 2021;7:206. [PMID: 34677292 DOI: 10.3390/jimaging7100206] [Reference Citation Analysis]
6 Maken P, Gupta A. 2D-to-3D: A Review for Computational 3D Image Reconstruction from X-ray Images. Arch Computat Methods Eng. [DOI: 10.1007/s11831-022-09790-z] [Reference Citation Analysis]
7 Loddo A, Buttau S, Di Ruberto C. Deep learning based pipelines for Alzheimer's disease diagnosis: A comparative study and a novel deep-ensemble method. Comput Biol Med 2021;:105032. [PMID: 34838263 DOI: 10.1016/j.compbiomed.2021.105032] [Cited by in Crossref: 6] [Cited by in F6Publishing: 5] [Article Influence: 6.0] [Reference Citation Analysis]
8 Lee S, Shin HJ, Kim S, Kim EK. Successful Implementation of an Artificial Intelligence-Based Computer-Aided Detection System for Chest Radiography in Daily Clinical Practice. Korean J Radiol 2022. [PMID: 35762186 DOI: 10.3348/kjr.2022.0193] [Reference Citation Analysis]
9 Stollmayer R, Budai BK, Rónaszéki A, Zsombor Z, Kalina I, Hartmann E, Tóth G, Szoldán P, Bérczi V, Maurovich-horvat P, Kaposi PN. Focal Liver Lesion MRI Feature Identification Using Efficientnet and MONAI: A Feasibility Study. Cells 2022;11:1558. [DOI: 10.3390/cells11091558] [Reference Citation Analysis]
10 Gidde PS, Prasad SS, Singh AP, Bhatheja N, Prakash S, Singh P, Saboo A, Takhar R, Gupta S, Saurav S, M V R, Singh A, Sardana V, Mahajan H, Kalyanpur A, Mandal AS, Mahajan V, Agrawal A, Agrawal A, Venugopal VK, Singh S, Dash D. Validation of expert system enhanced deep learning algorithm for automated screening for COVID-Pneumonia on chest X-rays. Sci Rep 2021;11:23210. [PMID: 34853342 DOI: 10.1038/s41598-021-02003-w] [Reference Citation Analysis]
11 Barzekar H, Yu Z. C-Net: A reliable convolutional neural network for biomedical image classification. Expert Systems with Applications 2022;187:116003. [DOI: 10.1016/j.eswa.2021.116003] [Cited by in Crossref: 5] [Cited by in F6Publishing: 4] [Article Influence: 5.0] [Reference Citation Analysis]
12 Meedeniya D, Kumarasinghe H, Kolonne S, Fernando C, Díez IDLT, Marques G. Chest X-ray analysis empowered with deep learning: A systematic review. Applied Soft Computing 2022;126:109319. [DOI: 10.1016/j.asoc.2022.109319] [Reference Citation Analysis]
13 Padash S, Mohebbian MR, Adams SJ, Henderson RDE, Babyn P. Pediatric chest radiograph interpretation: how far has artificial intelligence come? A systematic literature review. Pediatr Radiol 2022. [PMID: 35460035 DOI: 10.1007/s00247-022-05368-w] [Cited by in Crossref: 1] [Article Influence: 1.0] [Reference Citation Analysis]
14 Lee SY, Ha S, Jeon MG, Li H, Choi H, Kim HP, Choi YR, I H, Jeong YJ, Park YH, Ahn H, Hong SH, Koo HJ, Lee CW, Kim MJ, Kim YJ, Kim KW, Choi JM. Localization-adjusted diagnostic performance and assistance effect of a computer-aided detection system for pneumothorax and consolidation. NPJ Digit Med 2022;5:107. [PMID: 35908091 DOI: 10.1038/s41746-022-00658-x] [Reference Citation Analysis]
15 Tsuneki M. Deep learning models in medical image analysis. J Oral Biosci 2022:S1349-0079(22)00050-0. [PMID: 35306172 DOI: 10.1016/j.job.2022.03.003] [Cited by in Crossref: 2] [Cited by in F6Publishing: 2] [Article Influence: 2.0] [Reference Citation Analysis]
16 Sajun AR, Zualkernan I, Sankalpa D. Investigating the Performance of FixMatch for COVID-19 Detection in Chest X-rays. Applied Sciences 2022;12:4694. [DOI: 10.3390/app12094694] [Cited by in Crossref: 2] [Article Influence: 2.0] [Reference Citation Analysis]
17 Loddo A, Pili F, Di Ruberto C. Deep Learning for COVID-19 Diagnosis from CT Images. Applied Sciences 2021;11:8227. [DOI: 10.3390/app11178227] [Cited by in Crossref: 8] [Cited by in F6Publishing: 4] [Article Influence: 8.0] [Reference Citation Analysis]