Machine Learning Diffuse Optical Tomography Using Extreme Gradient Boosting and Genetic Programming

Hauptman, Ami; Balasubramaniam, Ganesh M.; Arnon, Shlomi

doi:10.3390/bioengineering10030382

Cited by 5 publications

(2 citation statements)

References 49 publications

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“…As a forward addition model, its core is to adopt the Boosting thought, which integrates multiple weak learners into a strong learner by a certain method, that is, multiple trees make decisions together, and the result of each tree is the difference between the target value and the predicted result of all the previous trees, adding up all the results to get the final result. In this way, the effect of the whole model is improved [ 17 ].…”

Section: Methodsmentioning

confidence: 99%

Machine learning-based models for prediction of the risk of stroke in coronary artery disease patients receiving coronary revascularization

Lin,

Ding,

et al. 2024

PLoS ONE

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Background To construct several prediction models for the risk of stroke in coronary artery disease (CAD) patients receiving coronary revascularization based on machine learning methods. Methods In total, 5757 CAD patients receiving coronary revascularization admitted to ICU in Medical Information Mart for Intensive Care IV (MIMIC-IV) were included in this cohort study. All the data were randomly split into the training set (n = 4029) and testing set (n = 1728) at 7:3. Pearson correlation analysis and least absolute shrinkage and selection operator (LASSO) regression model were applied for feature screening. Variables with Pearson correlation coefficient<9 were included, and the regression coefficients were set to 0. Features more closely related to the outcome were selected from the 10-fold cross-validation, and features with non-0 Coefficent were retained and included in the final model. The predictive values of the models were evaluated by sensitivity, specificity, area under the curve (AUC), accuracy, and 95% confidence interval (CI). Results The Catboost model presented the best predictive performance with the AUC of 0.831 (95%CI: 0.811–0.851) in the training set, and 0.760 (95%CI: 0.722–0.798) in the testing set. The AUC of the logistic regression model was 0.789 (95%CI: 0.764–0.814) in the training set and 0.731 (95%CI: 0.686–0.776) in the testing set. The results of Delong test revealed that the predictive value of the Catboost model was significantly higher than the logistic regression model (P<0.05). Charlson Comorbidity Index (CCI) was the most important variable associated with the risk of stroke in CAD patients receiving coronary revascularization. Conclusion The Catboost model was the optimal model for predicting the risk of stroke in CAD patients receiving coronary revascularization, which might provide a tool to quickly identify CAD patients who were at high risk of postoperative stroke.

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Section: Methodsmentioning

confidence: 99%

Machine learning-based models for prediction of the risk of stroke in coronary artery disease patients receiving coronary revascularization

Lin,

Ding,

et al. 2024

PLoS ONE

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“… 17 , 18 Researchers have extensively investigated the use of DOT in the diagnosis of breast cancer and the estimation of tissue optical properties. 19 – 24 By incorporating DOT into breast cancer diagnosis, we can potentially improve the accuracy of breast cancer detection and reduce the need for unnecessary biopsies, ultimately improving patient outcomes.…”

Section: Introductionmentioning

confidence: 99%

Improving diffuse optical tomography imaging quality using APU-Net: an attention-based physical U-Net model

Xue,

Li,

Zhu

2024

J. Biomed. Opt.

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Traditional diffuse optical tomography (DOT) reconstructions are hampered by image artifacts arising from factors such as DOT sources being closer to shallow lesions, poor optode-tissue coupling, tissue heterogeneity, and large high-contrast lesions lacking information in deeper regions (known as shadowing effect). Addressing these challenges is crucial for improving the quality of DOT images and obtaining robust lesion diagnosis.Aim: We address the limitations of current DOT imaging reconstruction by introducing an attention-based U-Net (APU-Net) model to enhance the image quality of DOT reconstruction, ultimately improving lesion diagnostic accuracy.Approach: We designed an APU-Net model incorporating a contextual transformer attention module to enhance DOT reconstruction. The model was trained on simulation and phantom data, focusing on challenges such as artifact-induced distortions and lesion-shadowing effects. The model was then evaluated by the clinical data.Results: Transitioning from simulation and phantom data to clinical patients' data, our APU-Net model effectively reduced artifacts with an average artifact contrast decrease of 26.83% and improved image quality. In addition, statistical analyses revealed significant contrast improvements in depth profile with an average contrast increase of 20.28% and 45.31% for the second and third target layers, respectively. These results highlighted the efficacy of our approach in breast cancer diagnosis. Conclusions:The APU-Net model improves the image quality of DOT reconstruction by reducing DOT image artifacts and improving the target depth profile.

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Roadmap on computational methods in optical imaging and holography [invited]

Rosen,

Alford,

Allan

et al. 2024

Appl. Phys. B

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Computational methods have been established as cornerstones in optical imaging and holography in recent years. Every year, the dependence of optical imaging and holography on computational methods is increasing significantly to the extent that optical methods and components are being completely and efficiently replaced with computational methods at low cost. This roadmap reviews the current scenario in four major areas namely incoherent digital holography, quantitative phase imaging, imaging through scattering layers, and super-resolution imaging. In addition to registering the perspectives of the modern-day architects of the above research areas, the roadmap also reports some of the latest studies on the topic. Computational codes and pseudocodes are presented for computational methods in a plug-and-play fashion for readers to not only read and understand but also practice the latest algorithms with their data. We believe that this roadmap will be a valuable tool for analyzing the current trends in computational methods to predict and prepare the future of computational methods in optical imaging and holography.

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Machine Learning Diffuse Optical Tomography Using Extreme Gradient Boosting and Genetic Programming

Cited by 5 publications

References 49 publications

Machine learning-based models for prediction of the risk of stroke in coronary artery disease patients receiving coronary revascularization

Machine learning-based models for prediction of the risk of stroke in coronary artery disease patients receiving coronary revascularization

Improving diffuse optical tomography imaging quality using APU-Net: an attention-based physical U-Net model

Roadmap on computational methods in optical imaging and holography [invited]

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