Integrating TANBN with cost sensitive classification algorithm for imbalanced data in medical diagnosis

Gan, Dan; Shen, Jiang; An, Bang; Xu, Man; Liu, Na

doi:10.1016/j.cie.2019.106266

Cited by 75 publications

(39 citation statements)

References 47 publications

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“…In future works, we attempt to explore data imbalance issue. The potential techniques include up/down sampling and cost‐ sensitive learning [22]. Besides, we will also investigate how to perform domain adaptation when source and target domains have different number of retinopathies, for instance the type of retinopathies in the target domain is a subset of that in the source domain.…”

Section: Conclusion and Discussionmentioning

confidence: 99%

Cross‐domain retinopathy classification with optical coherence tomography images via a novel deep domain adaptation method

Luo

Hou

et al. 2021

Journal of Biophotonics

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Deep learning based retinopathy classification with optical coherence tomography (OCT) images has recently attracted great attention. However, existing deep learning methods fail to work well when training and testing datasets are different due to the general issue of domain shift between datasets caused by different collection devices, subjects, imaging parameters, etc. To address this practical and challenging issue, we propose a novel deep domain adaptation (DDA) method to train a model on a labeled dataset and adapt it to an unlabelled dataset (collected under different conditions). It consists of two modules for domain alignment, that is, adversarial learning and entropy minimization. We conduct extensive experiments on three public datasets to evaluate the performance of the proposed method. The results indicate that there are large domain shifts between datasets, resulting a poor performance for conventional deep learning methods. The proposed DDA method can significantly outperform existing methods for retinopathy classification with OCT images. It achieves retinopathy classification accuracies of 0.915, 0.959 and 0.990 under three cross‐domain (cross‐dataset) scenarios. Moreover, it obtains a comparable performance with human experts on a dataset where no labeled data in this dataset have been used to train the proposed DDA method. We have also visualized the learnt features by using the t‐distributed stochastic neighbor embedding (t‐SNE) technique. The results demonstrate that the proposed method can learn discriminative features for retinopathy classification.

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Section: Conclusion and Discussionmentioning

confidence: 99%

Cross‐domain retinopathy classification with optical coherence tomography images via a novel deep domain adaptation method

Luo

Hou

et al. 2021

Journal of Biophotonics

View full text Add to dashboard Cite

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“…The best model without oversampling (ie, the LR model) also yielded remarkable findings (AUC 0.950; F1 0.604; sensitivity 0.764; specificity 0.919), and SMOTE oversampling further improved the model performance (AUC 0.960; F1 0.668; sensitivity 0.845; specificity 0.929). Considering the propensity of health care data to be imbalanced [51][52][53][54], our results suggest the need for further analysis of oversampling methods for medical data sets. Self-supervision [55,56] may also help improve the performance of models using imbalanced medical data sets; in particular, future studies should evaluate the impact of self-supervision on tabular medical data.…”

Section: Principal Findingsmentioning

confidence: 93%

Development and Validation of a Machine Learning Approach for Automated Severity Assessment of COVID-19 Based on Clinical and Imaging Data: Retrospective Study (Preprint)

Quiroz¹,

Feng²,

Cheng³

et al. 2020

Preprint

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BACKGROUND COVID-19 has overwhelmed health systems worldwide. It is important to identify severe cases as early as possible, such that resources can be mobilized and treatment can be escalated. OBJECTIVE This study aims to develop a machine learning approach for automated severity assessment of COVID-19 based on clinical and imaging data. METHODS Clinical data—including demographics, signs, symptoms, comorbidities, and blood test results—and chest computed tomography scans of 346 patients from 2 hospitals in the Hubei Province, China, were used to develop machine learning models for automated severity assessment in diagnosed COVID-19 cases. We compared the predictive power of the clinical and imaging data from multiple machine learning models and further explored the use of four oversampling methods to address the imbalanced classification issue. Features with the highest predictive power were identified using the Shapley Additive Explanations framework. RESULTS Imaging features had the strongest impact on the model output, while a combination of clinical and imaging features yielded the best performance overall. The identified predictive features were consistent with those reported previously. Although oversampling yielded mixed results, it achieved the best model performance in our study. Logistic regression models differentiating between mild and severe cases achieved the best performance for clinical features (area under the curve [AUC] 0.848; sensitivity 0.455; specificity 0.906), imaging features (AUC 0.926; sensitivity 0.818; specificity 0.901), and a combination of clinical and imaging features (AUC 0.950; sensitivity 0.764; specificity 0.919). The synthetic minority oversampling method further improved the performance of the model using combined features (AUC 0.960; sensitivity 0.845; specificity 0.929). CONCLUSIONS Clinical and imaging features can be used for automated severity assessment of COVID-19 and can potentially help triage patients with COVID-19 and prioritize care delivery to those at a higher risk of severe disease.

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“…We note that the best model without oversampling (LR) also yielded strong results (AUC: 0.950, F1: 0.604, sensitivity: 0.764, specificity: 0.919), and the SMOTE oversampling method improved the performance further (AUC: 0.960, F1: 0.668, sensitivity: 0.845, specificity: 0.929). Given the propensity of imbalanced data in healthcare, [44][45][46][47] our results suggest the need for further analysis of oversampling methods for medical datasets. Self-supervision, [48,49] may also help in improving performance on imbalanced medical datasets;…”

Section: Main Findingsmentioning

confidence: 93%

Severity Assessment of COVID-19 based on Clinical and Imaging Data

Quiroz

Feng

Cheng

et al. 2020

Preprint

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Objectives This study aims to develop a machine learning approach for automated severity assessment of COVID-19 patients based on clinical and imaging data. Materials and Methods Clinical data, including demographics, signs, symptoms, comorbidities and blood test results and chest CT scans of 346 patients from two hospitals in the Hubei province, China, were used to develop machine learning models for automated severity assessment of diagnosed COVID-19 cases. We compared the predictive power of clinical and imaging data by testing multiple machine learning models, and further explored the use of four oversampling methods to address the imbalance distribution issue. Features with the highest predictive power were identified using the SHAP framework. Results Targeting differentiation between mild and severe cases, logistic regression models achieved the best performance on clinical features (AUC:0.848, sensitivity:0.455, specificity:0.906), imaging features (AUC:0.926, sensitivity:0.818, specificity:0.901) and the combined features (AUC:0.950, sensitivity:0.764, specificity:0.919). The SMOTE oversampling method further improved the performance of the combined features to AUC of 0.960 (sensitivity:0.845, specificity:0.929). Discussion Imaging features had the strongest impact on the model output, while a combination of clinical and imaging features yielded the best performance overall. The identified predictive features were consistent with findings from previous studies. Oversampling yielded mixed results, although it achieved the best performance in our study. Conclusions This study indicates that clinical and imaging features can be used for automated severity assessment of COVID-19 patients and have the potential to assist with triaging COVID-19 patients and prioritizing care for patients at higher risk of severe cases. [Manuscript last updated on 31 July, 2020]

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Integrating TANBN with cost sensitive classification algorithm for imbalanced data in medical diagnosis

Cited by 75 publications

References 47 publications

Cross‐domain retinopathy classification with optical coherence tomography images via a novel deep domain adaptation method

Cross‐domain retinopathy classification with optical coherence tomography images via a novel deep domain adaptation method

Development and Validation of a Machine Learning Approach for Automated Severity Assessment of COVID-19 Based on Clinical and Imaging Data: Retrospective Study (Preprint)

Severity Assessment of COVID-19 based on Clinical and Imaging Data

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