Self-Attention Equipped Graph Convolutions for Disease Prediction

Kazi, Anees; Krishna, Sruthi; Shekarforoush, Shayan; Kortuem, Karsten; Albarqouni, Shadi; Navab, Nassir

doi:10.1109/isbi.2019.8759274

Cited by 38 publications

(52 citation statements)

References 9 publications

(21 reference statements)

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“…Geometric matrix completion (GMC): Geometric deep learning [32] is a novel field of deep learning, and has been introduced for computer-aided diagnosis in medicine only recently [33]. In previous work, we have shown that it is advantageous to construct multiple population graphs from meta-features of patients [34,35]. We further proposed GMC [36] (denoted in the following as SingleGMC) to alleviate the common problem of missing values in medical data sets [37].…”

Section: Classification Methodsmentioning

confidence: 99%

Modern machine-learning can support diagnostic differentiation of central and peripheral acute vestibular disorders

et al. 2020

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Background Diagnostic classification of central vs. peripheral etiologies in acute vestibular disorders remains a challenge in the emergency setting. Novel machine-learning methods may help to support diagnostic decisions. In the current study, we tested the performance of standard and machine-learning approaches in the classification of consecutive patients with acute central or peripheral vestibular disorders. Methods 40 Patients with vestibular stroke (19 with and 21 without acute vestibular syndrome (AVS), defined by the presence of spontaneous nystagmus) and 68 patients with peripheral AVS due to vestibular neuritis were recruited in the emergency department, in the context of the prospective EMVERT trial (EMergency VERTigo). All patients received a standardized neuro-otological examination including videooculography and posturography in the acute symptomatic stage and an MRI within 7 days after symptom onset. Diagnostic performance of state-of-the-art scores, such as HINTS (Head Impulse, gazeevoked Nystagmus, Test of Skew) and ABCD 2 (Age, Blood, Clinical features, Duration, Diabetes), for the differentiation of vestibular stroke vs. peripheral AVS was compared to various machine-learning approaches: (i) linear logistic regression (LR), (ii) non-linear random forest (RF), (iii) artificial neural network, and (iv) geometric deep learning (Single/MultiGMC). A prospective classification was simulated by tenfold cross-validation. We analyzed whether machine-estimated feature importances correlate with clinical experience. Results Machine-learning methods (e.g., MultiGMC) outperform univariate scores, such as HINTS or ABCD 2 , for differentiation of all vestibular strokes vs. peripheral AVS (MultiGMC area-under-the-curve (AUC): 0.96 vs. HINTS/ABCD 2 AUC: 0.71/0.58). HINTS performed similarly to MultiGMC for vestibular stroke with AVS (AUC: 0.86), but more poorly for vestibular stroke without AVS (AUC: 0.54). Machine-learning models learn to put different weights on particular features, each of which is relevant from a clinical viewpoint. Established non-linear machine-learning methods like RF and linear methods like LR are less powerful classification models (AUC: 0.89 vs. 0.62). Conclusions Established clinical scores (such as HINTS) provide a valuable baseline assessment for stroke detection in acute vestibular syndromes. In addition, machine-learning methods may have the potential to increase sensitivity and selectivity in the establishment of a correct diagnosis. Keywords Acute vestibular syndrome • HINTS • Machine-learning • MRI • Vestibular neuritis • Vestibular stroke Abbreviations ABCD 2 Age, blood pressure, clinical features, duration, diabetes ANN Artificial neural network AUC Area-under-the-curve AVS Acute vestibular syndrome CVRF Cardiovascular risk factors DT Decision tree DWI Diffusion weighted images ED Emergency department EMVERT EMergency VERTigo FLAIR Fluid attenuated inversion recovery GMC Geometric matrix completion HINTS Head impulse, gaze-evoked nystagmus, test of skew Seyed-Ahmad Ahmadi an...

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Section: Classification Methodsmentioning

confidence: 99%

Modern machine-learning can support diagnostic differentiation of central and peripheral acute vestibular disorders

et al. 2020

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“…Meanwhile, image data can be represented as a graph structure appropriate for the use of GNNs. Therefore, GNNs have an extensive application space in the field of medical imaging, such as image segmentation ( Gopinath et al, 2019 ; Wang et al, 2019b ; Tian et al, 2020a , b ), abnormal detection ( Wu et al, 2019 ) of MRI images and pathological images, classification ( Shi et al, 2019 ; Zhou et al, 2019 ; Adnan et al, 2020 ) and visualization ( Levy et al, 2020 ; Sureka et al, 2020 ) of histological images, analysis of surgical images ( Zhang et al, 2018 ), image enhancement ( Hu et al, 2020 ), registration ( Hansen et al, 2019 ), retrieval ( Zhai et al, 2019 ), brain connection ( Ktena et al, 2017 , 2018 ; Li X. et al, 2019a ; Mirakhorli and Mirakhorli, 2019 ; Grigis et al, 2020 ; Li et al, 2020 ; Zhang and Pierre, 2020 ; Zhang et al, 2021 ) and disease prediction ( Parisot et al, 2017 ; Kazi et al, 2018 , 2019a , b , c ; Anirudh and Thiagarajan, 2019 ; Yang et al, 2019 ; Stankevičiūtė et al, 2020 ; Zhang and Pierre, 2020 ; Zhang et al, 2021 ), etc.…”

Section: Typical Application Of Gnns In Bioinformaticsmentioning

confidence: 99%

“…Since then, the use of population graph methods for disease prediction has become the choice of many researchers. Kazi et al (2018) used a multi-level parallel GCN model to optimize the extraction of correlation information between nodes, which introduced an automatic learning layer for weight distribution and the attention mechanism for utilizing the features of each multimodal data ( Kazi et al, 2019c ). Aiming at the problem of insufficient feature extraction caused by fixed neighborhoods in GCN model, InceptionGCN ( Kazi et al, 2019a ) was proposed by Kazi et al, which considered the receptive field convolution kernels of different dimensions and utilized two aggregation methods to process all the features obtained by a convolution kernel.…”

Section: Typical Application Of Gnns In Bioinformaticsmentioning

confidence: 99%

Graph Neural Networks and Their Current Applications in Bioinformatics

et al. 2021

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Graph neural networks (GNNs), as a branch of deep learning in non-Euclidean space, perform particularly well in various tasks that process graph structure data. With the rapid accumulation of biological network data, GNNs have also become an important tool in bioinformatics. In this research, a systematic survey of GNNs and their advances in bioinformatics is presented from multiple perspectives. We first introduce some commonly used GNN models and their basic principles. Then, three representative tasks are proposed based on the three levels of structural information that can be learned by GNNs: node classification, link prediction, and graph generation. Meanwhile, according to the specific applications for various omics data, we categorize and discuss the related studies in three aspects: disease prediction, drug discovery, and biomedical imaging. Based on the analysis, we provide an outlook on the shortcomings of current studies and point out their developing prospect. Although GNNs have achieved excellent results in many biological tasks at present, they still face challenges in terms of low-quality data processing, methodology, and interpretability and have a long road ahead. We believe that GNNs are potentially an excellent method that solves various biological problems in bioinformatics research.

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“…As shown in Fig. 1(a), [8] concatenated the node embeddings directly, and both [12] and [22] adopted attention-based fusion mechanism like Fig. 1…”

Section: Introductionmentioning

confidence: 99%

Multi-modal Graph Learning for Disease Prediction

Zheng

Zhu

Liu

et al. 2021

Preprint

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Benefiting from the powerful expressive capability of graphs, graphbased approaches have achieved impressive performance in various biomedical applications. Most existing methods tend to define the adjacency matrix among samples manually based on meta-features, and then obtain the node embeddings for downstream tasks by Graph Representation Learning (GRL). However, it is not easy for these approaches to generalize to unseen samples. Meanwhile, the complex correlation between modalities is also ignored. As a result, these factors inevitably yield the inadequacy of providing valid information about the patient's condition for a reliable diagnosis. In this paper, we propose an end-to-end Multimodal Graph Learning framework (MMGL) for disease prediction. To effectively exploit the rich information across multi-modality associated to diseases, a modal-attentional multi-modal fusion is proposed to integrate the features of each modality by leveraging the correlation and complementarity between the modalities. Furthermore, instead of defining the adjacency matrix manually as existing methods, the latent graph structure can be captured through a novel way of adaptive graph learning. It could be jointly optimized with the prediction model, thus revealing the intrinsic connections among samples. Unlike the previous transductive methods, our model is also applicable to the scenario of inductive learning for those unseen data. An extensive group of experiments on two disease prediction problems is then carefully designed and presented, demonstrating that MMGL obtains more favorable performances. In addition, we also visualize and analyze the learned graph structure to provide more reliable decision support for doctor in real medical applications and inspiration for disease research.

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Self-Attention Equipped Graph Convolutions for Disease Prediction

Cited by 38 publications

References 9 publications

Modern machine-learning can support diagnostic differentiation of central and peripheral acute vestibular disorders

Modern machine-learning can support diagnostic differentiation of central and peripheral acute vestibular disorders

Graph Neural Networks and Their Current Applications in Bioinformatics

Multi-modal Graph Learning for Disease Prediction

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