Explainable diagnosis of secondary pulmonary tuberculosis by graph rank-based average pooling neural network

Wang, Shuihua; Govindaraj, Vishnuvarthanan; Górriz, Juan Manuel; Zhang, Xin; Zhang, Yudong

doi:10.1007/s12652-021-02998-0

Cited by 22 publications

(13 citation statements)

References 31 publications

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“…Two main categories of PTB are primary pulmonary tuberculosis (PPT) and secondary pulmonary tuberculosis (SPT). Wang et al [ 113 ] investigated the GCN model to recognize the SPT as many PTB cases are turned to be an SPT type. They proposed a rank-based pooling neural network (RAPNN) by which individual image-level features can be extracted, then integrated the GCN to RAPNN and built a new model called GRAPNN to identify the SPT.…”

Section: Case Studies Of Gnn For Medical Diagnosis and Analysismentioning

confidence: 99%

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

Ahmedt-Aristizabal

Armin

Denman

et al. 2021

Sensors

140

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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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Section: Case Studies Of Gnn For Medical Diagnosis and Analysismentioning

confidence: 99%

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

Ahmedt-Aristizabal

Armin

Denman

et al. 2021

Sensors

140

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“…The dataset was described in Ref. [15,16], of which the retrospective study was exempt by the Institutional Review Board of local hospitals. The data is available upon reasonable request to the corresponding authors.…”

Section: Datasetmentioning

confidence: 99%

Secondary Pulmonary Tuberculosis Identification Via pseudo-Zernike Moment and Deep Stacked Sparse Autoencoder

et al. 2021

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Secondary pulmonary tuberculosis (SPT) is one of the top ten causes of death from a single infectious agent. To recognize SPT more accurately, this paper proposes a novel artificial intelligence model, which uses Pseudo Zernike moment (PZM) as the feature extractor and deep stacked sparse autoencoder (DSSAE) as the classifier. In addition, 18-way data augmentation is employed to avoid overfitting. This model is abbreviated as PZM-DSSAE. The ten runs of 10-fold cross-validation show this model achieves a sensitivity of 93.33% ± 1.47%, a specificity of 93.13% ± 0.95%, a precision of 93.15% ± 0.89%, an accuracy of 93.23% ± 0.81%, and an F1 score of 93.23% ± 0.83%. The area-under-curve reaches 0.9739. This PZM-DSSAE is superior to 5 state-of-the-art approaches.

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“…Two main categories of PTB are primary pulmonary tuberculosis (PPT) and secondary pulmonary tuberculosis (SPT). Wang et al [158] investigated GCN model to recognize the SPT as many PTB cases are turned to be SPT type. They proposed a rank-based pooling neural network (RAPNN) by which individual image-level features can be extracted, then integrated the GCN to RAPNN and build a new model called GRAPNN to identify the SPT.…”

Section: ) Coronavirus 2 (Sars-cov-2 or Covid-19)mentioning

confidence: 99%

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

Ahmedt-Aristizabal,

Armin,

Denman

et al. 2021

Preprint

View full text Add to dashboard Cite

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 interactive nodes connected by edges whose weights can be 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.

show abstract

Explainable diagnosis of secondary pulmonary tuberculosis by graph rank-based average pooling neural network

Cited by 22 publications

References 31 publications

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

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

Secondary Pulmonary Tuberculosis Identification Via pseudo-Zernike Moment and Deep Stacked Sparse Autoencoder

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

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