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

Ahmedt-Aristizabal, David; Armin, Mohammad Ali; Denman, Simon; Fookes, Clinton; Petersson, Lars

doi:10.3390/s21144758

Cited by 136 publications

(72 citation statements)

References 203 publications

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“…It then leverages the shadow dataset and the corresponding predictions to train the surrogate model for model extraction attack, which has been formulated in Eq. (5). Following this framework, recent works [162,198] have investigated the GNN model extraction attacks with different levels of knowledge on shadow dataset and training graph of the target model.…”

Section: Methods Of Privacy Attack On Gnnsmentioning

confidence: 99%

“…For example, GNNs have been used to process electronic health record (EHR) of patients for diagnosis prediction [109]. GNNs are also deployed to better capture the graph signal of brain activity for medical analysis [5]. To ensure the privacy of sensitive data of patients, privacy-preserving GNNs are required for the applications in healthcare domain.…”

Section: Applications Of Privacy Preserving Gnnsmentioning

confidence: 99%

See 1 more Smart Citation

A Comprehensive Survey on Trustworthy Graph Neural Networks: Privacy, Robustness, Fairness, and Explainability

Dai¹,

Zhao²,

Zhu³

et al. 2022

Preprint

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Graph Neural Networks (GNNs) have made rapid developments in the recent years. Due to their great ability in modeling graph-structured data, GNNs are vastly used in various applications, including high-stakes scenarios such as financial analysis, traffic predictions, and drug discovery. Despite their great potential in benefiting humans in the real world, recent study shows that GNNs can leak private information, are vulnerable to adversarial attacks, can inherit and magnify societal bias from training data and lack interpretability, which have risk of causing unintentional harm to the users and society. For example, existing works demonstrate that attackers can fool the GNNs to give the outcome they desire with unnoticeable perturbation on training graph. GNNs trained on social networks may embed the discrimination in their decision process, strengthening the undesirable societal bias. Consequently, trustworthy GNNs in various aspects are emerging to prevent the harm from GNN models and increase the users' trust in GNNs. In this paper, we give a comprehensive survey of GNNs in the computational aspects of privacy, robustness, fairness, and explainability. For each aspect, we give the taxonomy of the related methods and formulate the general frameworks for the multiple categories of trustworthy GNNs. We also discuss the future research directions of each aspect and connections between these aspects to help achieve trustworthiness.

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Section: Methods Of Privacy Attack On Gnnsmentioning

confidence: 99%

Section: Applications Of Privacy Preserving Gnnsmentioning

confidence: 99%

A Comprehensive Survey on Trustworthy Graph Neural Networks: Privacy, Robustness, Fairness, and Explainability

Dai¹,

Zhao²,

Zhu³

et al. 2022

Preprint

View full text Add to dashboard Cite

show abstract

“…In fact, artificial intelligence, specifically machine learning and deep learning, has contributed to tackling multiple challenges in the diagnosis of different diseases. Since their emergence, a wide range of research has been carried out with breakthrough results [2][3][4][5]. When symptoms are visible, images of the infected area are collected, and computer vision and image processing algorithms and techniques are applied to extract features that are fed to the diagnostic models.…”

Section: Introductionmentioning

confidence: 99%

“…Instead, they check both areas simultaneously for further assurance. Moreover, previous studies in this domain have not applied deep learning and transfer learning models, despite their success in the diagnosis of different diseases [2][3][4][5]. In this work, we seek to fill this gap.…”

Section: Introductionmentioning

confidence: 99%

Neonatal Jaundice Diagnosis Using a Smartphone Camera Based on Eye, Skin, and Fused Features with Transfer Learning

Althnian

Almanea

Aloboud

2021

Sensors

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Neonatal jaundice is a common condition worldwide. Failure of timely diagnosis and treatment can lead to death or brain injury. Current diagnostic approaches include a painful and time-consuming invasive blood test and non-invasive tests using costly transcutaneous bilirubinometers. Since periodic monitoring is crucial, multiple efforts have been made to develop non-invasive diagnostic tools using a smartphone camera. However, existing works rely either on skin or eye images using statistical or traditional machine learning methods. In this paper, we adopt a deep transfer learning approach based on eye, skin, and fused images. We also trained well-known traditional machine learning models, including multi-layer perceptron (MLP), support vector machine (SVM), decision tree (DT), and random forest (RF), and compared their performance with that of the transfer learning model. We collected our dataset using a smartphone camera. Moreover, unlike most of the existing contributions, we report accuracy, precision, recall, f-score, and area under the curve (AUC) for all the experiments and analyzed their significance statistically. Our results indicate that the transfer learning model performed the best with skin images, while traditional models achieved the best performance with eyes and fused features. Further, we found that the transfer learning model with skin features performed comparably to the MLP model with eye features.

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“…Real-world graphs, such as medical and economic networks, are associated with sensitive information about individuals and their activities and cannot always be made public. Releasing pre-trained models provides an opportunity for using the private knowledge beyond company boundaries [2]. However, recent works have shown that GNNs are vulnerable to membership inference attacks [9,18].…”

mentioning

confidence: 99%

Releasing Graph Neural Networks with Differential Privacy Guarantees

Olatunji,

Funke,

Khosla

2021

Preprint

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With the increasing popularity of Graph Neural Networks (GNNs) in several sensitive applications like healthcare and medicine, concerns have been raised over the privacy aspects of trained GNNs. More notably, GNNs are vulnerable to privacy attacks, such as membership inference attacks, even if only blackbox access to the trained model is granted. To build defenses, differential privacy has emerged as a mechanism to disguise the sensitive data in training datasets. Following the strategy of Private Aggregation of Teacher Ensembles (PATE), recent methods leverage a large ensemble of teacher models. These teachers are trained on disjoint subsets of private data and are employed to transfer knowledge to a student model, which is then released with privacy guarantees. However, splitting graph data into many disjoint training sets may destroy the structural information and adversely affect accuracy. We propose a new graph-specific scheme of releasing a student GNN, which avoids splitting private training data altogether. The student GNN is trained using public data, partly labeled privately using the teacher GNN models trained exclusively for each query node. We theoretically analyze our approach in the Rènyi differential privacy framework and provide privacy guarantees. Besides, we show the solid experimental performance of our method compared to several baselines, including the PATE baseline adapted for graph-structured data. Our anonymized code is available.

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

Cited by 136 publications

References 203 publications

A Comprehensive Survey on Trustworthy Graph Neural Networks: Privacy, Robustness, Fairness, and Explainability

A Comprehensive Survey on Trustworthy Graph Neural Networks: Privacy, Robustness, Fairness, and Explainability

Neonatal Jaundice Diagnosis Using a Smartphone Camera Based on Eye, Skin, and Fused Features with Transfer Learning

Releasing Graph Neural Networks with Differential Privacy Guarantees

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