Graph-Based Deep Learning for Medical Diagnosis and Analysis: Past, Present and Future

被引:131
作者
Ahmedt-Aristizabal, David [1 ,2 ]
Armin, Mohammad Ali [1 ]
Denman, Simon [2 ]
Fookes, Clinton [2 ]
Petersson, Lars [1 ]
机构
[1] CSIRO Data61, Imaging & Comp Vis Grp, Canberra, ACT 2601, Australia
[2] Queensland Univ Technol, Signal Proc Artificial Intelligence & Vis Technol, Brisbane, Qld 4000, Australia
关键词
graph representation; graph convolutional networks; brain functional connectivity; anatomical structure analysis; CONVOLUTIONAL NEURAL-NETWORKS; NEUROIMAGING INITIATIVE ADNI; ALZHEIMERS-DISEASE; SEGMENTATION ALGORITHMS; BRAIN NETWORK; MRI; CLASSIFICATION; CONNECTIVITY; PREDICTION; ARCHIVE;
D O I
10.3390/s21144758
中图分类号
O65 [分析化学];
学科分类号
070302 ; 081704 ;
摘要
With the advances of data-driven machine learning research, a wide variety of prediction problems have been tackled. It has become critical to explore how machine learning and specifically deep learning methods can be exploited to analyse healthcare data. A major limitation of existing methods has been the focus on grid-like data; however, the structure of physiological recordings are often irregular and unordered, which makes it difficult to conceptualise them as a matrix. As such, graph neural networks have attracted significant attention by exploiting implicit information that resides in a biological system, with interacting nodes connected by edges whose weights can be determined by either temporal associations or anatomical junctions. In this survey, we thoroughly review the different types of graph architectures and their applications in healthcare. We provide an overview of these methods in a systematic manner, organized by their domain of application including functional connectivity, anatomical structure, and electrical-based analysis. We also outline the limitations of existing techniques and discuss potential directions for future research.
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页数:48
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