Unbox the black-box for the medical explainable AI via multi-modal and multi-centre data fusion: A mini-review, two showcases and beyond

Yang, Guang; Ye, Qinghao; Xia, Jun

doi:10.1016/j.inffus.2021.07.016

Cited by 433 publications

(203 citation statements)

References 138 publications

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“…We propose a segmentation model that can automatically segment the CT images and MRI images regardless of the thickness. In our previous research, the feasibility of this model was verified [ 21 , 29 ].…”

Section: Methodsmentioning

confidence: 99%

AI-based medical e-diagnosis for fast and automatic ventricular volume measurement in patients with normal pressure hydrocephalus

Zhou

Yang

et al. 2022

Neural Comput & Applic

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Based on CT and MRI images acquired from normal pressure hydrocephalus (NPH) patients, using machine learning methods, we aim to establish a multimodal and high-performance automatic ventricle segmentation method to achieve an efficient and accurate automatic measurement of the ventricular volume. First, we extract the brain CT and MRI images of 143 definite NPH patients. Second, we manually label the ventricular volume (VV) and intracranial volume (ICV). Then, we use the machine learning method to extract features and establish automatic ventricle segmentation model. Finally, we verify the reliability of the model and achieved automatic measurement of VV and ICV. In CT images, the Dice similarity coefficient (DSC), intraclass correlation coefficient (ICC), Pearson correlation, and Bland–Altman analysis of the automatic and manual segmentation result of the VV were 0.95, 0.99, 0.99, and 4.2 ± 2.6, respectively. The results of ICV were 0.96, 0.99, 0.99, and 6.0 ± 3.8, respectively. The whole process takes 3.4 ± 0.3 s. In MRI images, the DSC, ICC, Pearson correlation, and Bland–Altman analysis of the automatic and manual segmentation result of the VV were 0.94, 0.99, 0.99, and 2.0 ± 0.6, respectively. The results of ICV were 0.93, 0.99, 0.99, and 7.9 ± 3.8, respectively. The whole process took 1.9 ± 0.1 s. We have established a multimodal and high-performance automatic ventricle segmentation method to achieve efficient and accurate automatic measurement of the ventricular volume of NPH patients. This can help clinicians quickly and accurately understand the situation of NPH patient's ventricles.

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Section: Methodsmentioning

confidence: 99%

AI-based medical e-diagnosis for fast and automatic ventricular volume measurement in patients with normal pressure hydrocephalus

Zhou

Yang

et al. 2022

Neural Comput & Applic

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“…According to [21,22], post hoc explainability methods can be categorized as: dimension reduction, attention mechanism, restricted neural network architecture, text explanation, visual explanation, local explanation, explanation by example, explanation by simplification and feature relevance. The taxonomy of XAI methods is shown in Figure 2.…”

Section: Related Explainable Artificial Intelligence Conceptsmentioning

confidence: 99%

“…Other post hoc methods include SHapley Additive exPlanations (SHAP) [ 17 ], class activation mapping (CAM) [ 18 ], principle component analysis (PCA) [ 19 ] and Gradient-weighted class activation mapping (Grad-CAM) [ 20 ]. According to [ 21 , 22 ], post hoc explainability methods can be categorized as: dimension reduction, attention mechanism, restricted neural network architecture, text explanation, visual explanation, local explanation, explanation by example, explanation by simplification and feature relevance. The taxonomy of XAI methods is shown in Figure 2 .…”

Section: Introductionmentioning

confidence: 99%

Applications of Explainable Artificial Intelligence in Diagnosis and Surgery

2022

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In recent years, artificial intelligence (AI) has shown great promise in medicine. However, explainability issues make AI applications in clinical usages difficult. Some research has been conducted into explainable artificial intelligence (XAI) to overcome the limitation of the black-box nature of AI methods. Compared with AI techniques such as deep learning, XAI can provide both decision-making and explanations of the model. In this review, we conducted a survey of the recent trends in medical diagnosis and surgical applications using XAI. We have searched articles published between 2019 and 2021 from PubMed, IEEE Xplore, Association for Computing Machinery, and Google Scholar. We included articles which met the selection criteria in the review and then extracted and analyzed relevant information from the studies. Additionally, we provide an experimental showcase on breast cancer diagnosis, and illustrate how XAI can be applied in medical XAI applications. Finally, we summarize the XAI methods utilized in the medical XAI applications, the challenges that the researchers have met, and discuss the future research directions. The survey result indicates that medical XAI is a promising research direction, and this study aims to serve as a reference to medical experts and AI scientists when designing medical XAI applications.

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“…Recently, plenty of COVID-19 recognition algorithms have been proposed, which have adopted artificial intelligence algorithms especially using the CNN. A comprehensive review of artificial intelligence assisted COVID-19 detection and diagnosis can be found elsewhere [22] , [23] , [24] , [25] , [26] , and here we only provided a summary for the most relevant studies.…”

Section: Related Workmentioning

confidence: 99%

Robust weakly supervised learning for COVID-19 recognition using multi-center CT images

Gao

Ding

et al. 2022

Applied Soft Computing

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The world is currently experiencing an ongoing pandemic of an infectious disease named coronavirus disease 2019 (i.e., COVID-19), which is caused by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). Computed Tomography (CT) plays an important role in assessing the severity of the infection and can also be used to identify those symptomatic and asymptomatic COVID-19 carriers. With a surge of the cumulative number of COVID-19 patients, radiologists are increasingly stressed to examine the CT scans manually. Therefore, an automated 3D CT scan recognition tool is highly in demand since the manual analysis is time-consuming for radiologists and their fatigue can cause possible misjudgment. However, due to various technical specifications of CT scanners located in different hospitals, the appearance of CT images can be significantly different leading to the failure of many automated image recognition approaches. The multi-domain shift problem for the multi-center and multi-scanner studies is therefore nontrivial that is also crucial for a dependable recognition and critical for reproducible and objective diagnosis and prognosis. In this paper, we proposed a COVID-19 CT scan recognition model namely coronavirus information fusion and diagnosis network (CIFD-Net) that can efficiently handle the multi-domain shift problem via a new robust weakly supervised learning paradigm. Our model can resolve the problem of different appearance in CT scan images reliably and efficiently while attaining higher accuracy compared to other state-of-the-art methods.

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Unbox the black-box for the medical explainable AI via multi-modal and multi-centre data fusion: A mini-review, two showcases and beyond

Cited by 433 publications

References 138 publications

AI-based medical e-diagnosis for fast and automatic ventricular volume measurement in patients with normal pressure hydrocephalus

AI-based medical e-diagnosis for fast and automatic ventricular volume measurement in patients with normal pressure hydrocephalus

Applications of Explainable Artificial Intelligence in Diagnosis and Surgery

Robust weakly supervised learning for COVID-19 recognition using multi-center CT images

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