Correction to: Decoding COVID-19 pneumonia: comparison of deep learning and radiomics CT image signatures

Wang, Hongmei; Wang, Lu; Lee, Edward H.; Zheng, Jimmy; Zhang, Wei; Halabi, Safwan; Deng, Kan; Song, Jiangdian; Yeom, Kristen W.

doi:10.1007/s00259-021-05268-5

Cited by 2 publications

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“…The use of XAI for a binary decision regarding presence or absence of a disease is of great help, allowing more complex cases which are difficult to classify to be determined in a clinical setting by a human expert [281]. The radiomics CT image signatures and DNN methods were used to gain insight into CT image features that are important for COVID-19 prediction [282]. The features from DL and radiologists were compared to gain an understanding of the ML algorithm interpretability for improving human diagnostic performance [282].…”

Section: Human-in-the-loopmentioning

confidence: 99%

See 1 more Smart Citation

Survey of explainable artificial intelligence techniques for biomedical imaging with deep neural networks

Nazir¹,

Dickson²,

Akram³

2023

Computers in Biology and Medicine

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Section: Human-in-the-loopmentioning

confidence: 99%

“…The radiomics CT image signatures and DNN methods were used to gain insight into CT image features that are important for COVID-19 prediction [282]. The features from DL and radiologists were compared to gain an understanding of the ML algorithm interpretability for improving human diagnostic performance [282].…”

Section: Human-in-the-loopmentioning

confidence: 99%

Survey of explainable artificial intelligence techniques for biomedical imaging with deep neural networks

Nazir¹,

Dickson²,

Akram³

2023

Computers in Biology and Medicine

View full text Add to dashboard Cite

Differentiation of COVID‐19 pneumonia from other lung diseases using CT radiomic features and machine learning: A large multicentric cohort study

Shiri,

Salimi,

Saberi

et al. 2024

Int J Imaging Syst Tech

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To derive and validate an effective machine learning and radiomics‐based model to differentiate COVID‐19 pneumonia from other lung diseases using a large multi‐centric dataset. In this retrospective study, we collected 19 private and five public datasets of chest CT images, accumulating to 26 307 images (15 148 COVID‐19; 9657 other lung diseases including non‐COVID‐19 pneumonia, lung cancer, pulmonary embolism; 1502 normal cases). We tested 96 machine learning‐based models by cross‐combining four feature selectors (FSs) and eight dimensionality reduction techniques with eight classifiers. We trained and evaluated our models using three different strategies: #1, the whole dataset (15 148 COVID‐19 and 11 159 other); #2, a new dataset after excluding healthy individuals and COVID‐19 patients who did not have RT‐PCR results (12 419 COVID‐19 and 8278 other); and #3 only non‐COVID‐19 pneumonia patients and a random sample of COVID‐19 patients (3000 COVID‐19 and 2582 others) to provide balanced classes. The best models were chosen by one‐standard‐deviation rule in 10‐fold cross‐validation and evaluated on the hold out test sets for reporting. In strategy#1, Relief FS combined with random forest (RF) classifier resulted in the highest performance (accuracy = 0.96, AUC = 0.99, sensitivity = 0.98, specificity = 0.94, PPV = 0.96, and NPV = 0.96). In strategy#2, Recursive Feature Elimination (RFE) FS and RF classifier combination resulted in the highest performance (accuracy = 0.97, AUC = 0.99, sensitivity = 0.98, specificity = 0.95, PPV = 0.96, NPV = 0.98). Finally, in strategy #3, the ANOVA FS and RF classifier combination resulted in the highest performance (accuracy = 0.94, AUC =0.98, sensitivity = 0.96, specificity = 0.93, PPV = 0.93, NPV = 0.96). Lung radiomic features combined with machine learning algorithms can enable the effective diagnosis of COVID‐19 pneumonia in CT images without the use of additional tests.

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Correction to: Decoding COVID-19 pneumonia: comparison of deep learning and radiomics CT image signatures

Cited by 2 publications

References 0 publications

Survey of explainable artificial intelligence techniques for biomedical imaging with deep neural networks

Survey of explainable artificial intelligence techniques for biomedical imaging with deep neural networks

Differentiation of COVID‐19 pneumonia from other lung diseases using CT radiomic features and machine learning: A large multicentric cohort study

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