An explainable deep learning framework for characterizing and interpreting human brain states

Zhang, Shu; Wang, Junxin; Yu, Sigang; Wang, Ruoyang; Han, Junwei; Zhao, Shijie; Liu, Tianming; Lv, Jinglei

doi:10.1016/j.media.2022.102665

Cited by 10 publications

(4 citation statements)

References 37 publications

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Section: Brain Functional Mri (Fmri)mentioning

confidence: 99%

“…Recently, XAI techniques have been heavily involved in fMRI-related studies on revealing the dysfunctional ROIs related to brain diseases [80][81][82][83][84][85][86]. For example, Zhang et al classified seven types of brain tasks using a knowledge-informed self-attention graph-pooling-based (SAGPool) GCN [81]. The model took the fMRI BOLD signals as node features and the binarized connectivity matrix of the functional connectomes as the graph for performing a graph classification.…”

Section: Brain Functional Mri (Fmri)mentioning

confidence: 99%

See 1 more Smart Citation

Recent Advances in Explainable Artificial Intelligence for Magnetic Resonance Imaging

Qian

Wang

et al. 2023

Diagnostics

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Advances in artificial intelligence (AI), especially deep learning (DL), have facilitated magnetic resonance imaging (MRI) data analysis, enabling AI-assisted medical image diagnoses and prognoses. However, most of the DL models are considered as “black boxes”. There is an unmet need to demystify DL models so domain experts can trust these high-performance DL models. This has resulted in a sub-domain of AI research called explainable artificial intelligence (XAI). In the last decade, many experts have dedicated their efforts to developing novel XAI methods that are competent at visualizing and explaining the logic behind data-driven DL models. However, XAI techniques are still in their infancy for medical MRI image analysis. This study aims to outline the XAI applications that are able to interpret DL models for MRI data analysis. We first introduce several common MRI data modalities. Then, a brief history of DL models is discussed. Next, we highlight XAI frameworks and elaborate on the principles of multiple popular XAI methods. Moreover, studies on XAI applications in MRI image analysis are reviewed across the tissues/organs of the human body. A quantitative analysis is conducted to reveal the insights of MRI researchers on these XAI techniques. Finally, evaluations of XAI methods are discussed. This survey presents recent advances in the XAI domain for explaining the DL models that have been utilized in MRI applications.

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Section: Brain Functional Mri (Fmri)mentioning

confidence: 99%

Section: Brain Functional Mri (Fmri)mentioning

confidence: 99%

Recent Advances in Explainable Artificial Intelligence for Magnetic Resonance Imaging

Qian

Wang

et al. 2023

Diagnostics

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show abstract

“…Machine learning can independently replicates human cognition and make decisions based on its perceived environment to achieve predetermined goals (8). Zhang S et al proposed an interpretable deep learning framework for describing and interpreting human brain states (9). Furthermore, Lee C et al developed a novel machine learning model for predicting nonmetastatic PCa in men based on the Surveillance, Epidemiology, and End Results (SEER) database (10).…”

Section: Introductionmentioning

confidence: 99%

“…Zhang S et al. proposed an interpretable deep learning framework for describing and interpreting human brain states ( 9 ). Furthermore, Lee C et al.…”

Section: Introductionmentioning

confidence: 99%

Development, comparison, and validation of four intelligent, practical machine learning models for patients with prostate-specific antigen in the gray zone

et al. 2023

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PurposeMachine learning prediction models based on LogisticRegression, XGBoost, GaussianNB, and LGBMClassifier for patients in the prostate-specific antigen gray zone are to be developed and compared, identifying valuable predictors. Predictive models are to be integrated into actual clinical decisions.MethodsPatient information was collected from December 01, 2014 to December 01, 2022 from the Department of Urology, The First Affiliated Hospital of Nanchang University. Patients with a pathological diagnosis of prostate hyperplasia or prostate cancer (any PCa) and having a prostate-specific antigen (PSA) level of 4–10 ng/mL before prostate puncture were included in the initial information collection. Eventually, 756 patients were selected. Age, total prostate-specific antigen (tPSA), free prostate-specific antigen (fPSA), fPSA/tPSA, prostate volume (PV), prostate-specific antigen density (PSAD), (fPSA/tPSA)/PSAD, and the prostate MRI results of these patients were recorded. After univariate and multivariate logistic analyses, statistically significant predictors were screened to build and compare machine learning models based on LogisticRegression, XGBoost, GaussianNB, and LGBMClassifier to determine more valuable predictors.ResultsMachine learning prediction models based on LogisticRegression, XGBoost, GaussianNB, and LGBMClassifier exhibit higher predictive power than individual metrics. The area under the curve (AUC) (95% CI), accuracy, sensitivity, specificity, positive predictive value, negative predictive value, and F1 score of the LogisticRegression machine learning prediction model were 0.932 (0.881–0.983), 0.792, 0.824, 0.919, 0.652, 0.920, and 0.728, respectively; of the XGBoost machine learning prediction model were 0.813 (0.723–0.904), 0.771, 0.800, 0.768, 0.737, 0.793 and 0.767, respectively; of the GaussianNB machine learning prediction model were 0.902 (0.843–0.962), 0.813, 0.875, 0.819, 0.600, 0.909, and 0.712, respectively; and of the LGBMClassifier machine learning prediction model were 0.886 (0.809–0.963), 0.833, 0.882, 0.806, 0.725, 0.911, and 0.796, respectively. The LogisticRegression machine learning prediction model has the highest AUC among all prediction models, and the difference between the AUC of the LogisticRegression prediction model and those of XGBoost, GaussianNB, and LGBMClassifier is statistically significant (p < 0.001).ConclusionMachine learning prediction models based on LogisticRegression, XGBoost, GaussianNB, and LGBMClassifier algorithms exhibit superior predictability for patients in the PSA gray area, with the LogisticRegression model yielding the best prediction. The aforementioned predictive models can be used for actual clinical decision-making.

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Unlocking the black box: an in-depth review on interpretability, explainability, and reliability in deep learning

ŞAHiN,

Arslan,

Özdemir

2024

Neural Comput & Applic

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An explainable deep learning framework for characterizing and interpreting human brain states

Cited by 10 publications

References 37 publications

Recent Advances in Explainable Artificial Intelligence for Magnetic Resonance Imaging

Recent Advances in Explainable Artificial Intelligence for Magnetic Resonance Imaging

Development, comparison, and validation of four intelligent, practical machine learning models for patients with prostate-specific antigen in the gray zone

Unlocking the black box: an in-depth review on interpretability, explainability, and reliability in deep learning

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