Unbox the black-box for the medical explainable AI via multi-modal and multi-centre data fusion: a mini-review, two showcases and beyond
File(s)
Published version
Author(s)
Yang, Guang
Ye, Qinghao
Xia, Jun
Type
Journal Article
Abstract
Explainable Artificial Intelligence (XAI) is an emerging research topic of machine learning aimed at unboxing how AI systems’ black-boxchoices are made.This research field inspects the measures and models involved in decision-making and seeks solutions to explain them explicitly. Many of the machine learning algorithms can not manifest how and why a decision has been cast. This is particularly true of the most popular deep neural network approaches currently in use. Consequently, our confidence in AI systems can be hindered by the lack of explainability in these black-box models. The XAI becomes more and more cru-cial for deep learning powered applications, especially for medical and healthcare studies, although in general these deep neural networks can return an arresting dividend in performance. The insufficient explainability and transparency in most existing AI systems can be one of the major reasons that successful implemen-tation and integration of AI tools into routine clinical practice are uncommon. In this study, we first surveyed the current progress of XAI and in particular itsadvances in healthcare applications. We then introduced our solutions for XAI leveraging multi-modal and multi-centre data fusion, and subsequently validated in two showcases following real clinical scenarios. Comprehensive quantitative and qualitative analyses can prove the efficacy of our proposed XAI solutions, from which we can envisage successful applications in a broader range of clinical questions.
Date Issued
2022-01
Date Acceptance
2021-07-25
Citation
Information Fusion, 2022, 77, pp.29-52
ISSN
1566-2535
Publisher
Elsevier
Start Page
29
End Page
52
Journal / Book Title
Information Fusion
Volume
77
Copyright Statement
© 2021 The Authors. Published by Elsevier B.V. This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).
License URL
Sponsor
British Heart Foundation
European Research Council Horizon 2020
Commission of the European Communities
Innovative Medicines Initiative
Boehringer Ingelheim Ltd
Medical Research Council (MRC)
Identifier
https://www.sciencedirect.com/science/article/pii/S1566253521001597
Grant Number
PG/16/78/32402
H2020-SC1-FA-DTS-2019-1 952172
101005122
101005122
PO:4700244755 Study:1199-0457
MR/V023799/1
Subjects
Science & Technology
Technology
Computer Science, Artificial Intelligence
Computer Science, Theory & Methods
Computer Science
Explainable AI
Information fusion
Multi-domain information fusion
Weakly supervised learning
Medical image analysis
ARTIFICIAL-INTELLIGENCE
DIAGNOSTIC ERRORS
CARE
SYSTEM
FUTURE
PERFORMANCE
PREDICTION
PROGNOSIS
Explainable AI
Information fusion
Medical image analysis
Multi-domain information fusion
Weakly supervised learning
cs.AI
cs.AI
cs.CV
cs.IT
cs.LG
math.IT
Artificial Intelligence & Image Processing
0801 Artificial Intelligence and Image Processing
Publication Status
Published
Date Publish Online
2021-07-31