Novel Effective Connectivity Inference Using Ultra-Group Constrained Orthogonal Forward Regression and Elastic Multilayer Perceptron Classifier for MCI Identification

Li, Yang; Yang, Hao; Lei, Baiying; Liu, Jingyu; Wee, Chong-Yaw

doi:10.1109/tmi.2018.2882189

Cited by 45 publications

(21 citation statements)

References 39 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In this experiment, we use FedResNet, centralized ResNet (He et al 2016 ), Convolutional Neural Network (CNN) (Tajbakhsh et al 2016 ), Multilayer Perceptron (MLP) (Li et al 2018 ), K Nearest Neighbors (KNN) (Park and Lee 2018 ), and Support Vector Machines (SVM) (Morra et al 2009 ) as our baseline models that proves FedDPGAN performance. Note that we apply ResNet model in our FedDPGAN framework.…”

Section: Resultsmentioning

confidence: 96%

FedDPGAN: Federated Differentially Private Generative Adversarial Networks Framework for the Detection of COVID-19 Pneumonia

et al. 2021

View full text Add to dashboard Cite

Existing deep learning technologies generally learn the features of chest X-ray data generated by Generative Adversarial Networks (GAN) to diagnose COVID-19 pneumonia. However, the above methods have a critical challenge: data privacy. GAN will leak the semantic information of the training data which can be used to reconstruct the training samples by attackers, thereby this method will leak the privacy of the patient. Furthermore, for this reason, that is the limitation of the training data sample, different hospitals jointly train the model through data sharing, which will also cause privacy leakage. To solve this problem, we adopt the Federated Learning (FL) framework, a new technique being used to protect data privacy. Under the FL framework and Differentially Private thinking, we propose a Federated Differentially Private Generative Adversarial Network (FedDPGAN) to detect COVID-19 pneumonia for sustainable smart cities. Specifically, we use DP-GAN to privately generate diverse patient data in which differential privacy technology is introduced to make sure the privacy protection of the semantic information of the training dataset. Furthermore, we leverage FL to allow hospitals to collaboratively train COVID-19 models without sharing the original data. Under Independent and Identically Distributed (IID) and non-IID settings, the evaluation of the proposed model is on three types of chest X-ray (CXR)images dataset (COVID-19, normal, and normal pneumonia). A large number of truthful reports make the verification of our model can effectively diagnose COVID-19 without compromising privacy.

show abstract

Section: Resultsmentioning

confidence: 96%

FedDPGAN: Federated Differentially Private Generative Adversarial Networks Framework for the Detection of COVID-19 Pneumonia

et al. 2021

View full text Add to dashboard Cite

show abstract

“…Such a group constrained sparse network can simultaneously make a common region of interest (ROI) selection across subjects and optimally estimate the ROI time series under consideration. Li et al (Li et al, 2018) used a new sparse constrained connectivity inference method and an elastic multilayer perceptron classifier (MPC) to identify MCI. The topology of the network connection is effectively identified by considering the weak derivative information of the data.…”

Section: Introductionmentioning

confidence: 99%

Self-calibrated brain network estimation and joint non-convex multi-task learning for identification of early Alzheimer's disease

Lei

Cheng

Frangi

et al. 2020

Medical Image Analysis

Self Cite

View full text Add to dashboard Cite

show abstract

“…Overall, performance was satisfactory when discriminating between AD and controls (accuracies between 85-97%), but dropped when discriminating between MCI and controls (70-88%). Most studies reported the nodes which contribute most to discrimination between AD and controls: there is some heterogeneity, but most often components of the default mode network (DMN) were identified (Yang Li et al 2019;Nguyen et al 2019;Jin et al 2020;M. Wang et al 2020).…”

Section: Functional Mrimentioning

confidence: 99%

Artificial intelligence for diagnosis and prognosis in neuroimaging for dementia; a systematic review

Borchert

Azevedo

Badhwar

et al. 2021

Preprint

View full text Add to dashboard Cite

Introduction Recent developments in artificial intelligence (AI) and neuroimaging offer new opportunities for improving diagnosis and prognosis of dementia. To synthesise the available literature, we performed a systematic review. Methods We systematically reviewed primary research publications up to January 2021, using AI for neuroimaging to predict diagnosis and/or prognosis in cognitive neurodegenerative diseases. After initial screening, data from each study was extracted, including: demographic information, AI methods, neuroimaging features, and results. Results We found 2709 reports, with 252 eligible papers remaining following screening. Most studies relied on the Alzheimers Disease Neuroimaging Initiative (ADNI) dataset (n=178) with no other individual dataset used more than 5 times. Algorithmic classifiers, such as support vector machine (SVM), were the most commonly used AI method (47%) followed by discriminative (32%) and generative (11%) classifiers. Structural MRI was used in 71% of studies with a wide range of accuracies for the diagnosis of neurodegenerative diseases and predicting prognosis. Lower accuracy was found in studies using a multi-class classifier or an external cohort as the validation group. There was improvement in accuracy when neuroimaging modalities were combined, e.g. PET and structural MRI. Only 17 papers studied non-Alzheimers disease dementias. Conclusion The use of AI with neuroimaging for diagnosis and prognosis in dementia is a rapidly emerging field. We make a number of recommendations addressing the definition of key clinical questions, heterogeneity of AI methods, and the availability of appropriate and representative data. We anticipate that addressing these issues will enable the field to move towards meaningful clinical translation.

show abstract

Novel Effective Connectivity Inference Using Ultra-Group Constrained Orthogonal Forward Regression and Elastic Multilayer Perceptron Classifier for MCI Identification

Cited by 45 publications

References 39 publications

FedDPGAN: Federated Differentially Private Generative Adversarial Networks Framework for the Detection of COVID-19 Pneumonia

FedDPGAN: Federated Differentially Private Generative Adversarial Networks Framework for the Detection of COVID-19 Pneumonia

Self-calibrated brain network estimation and joint non-convex multi-task learning for identification of early Alzheimer's disease

Artificial intelligence for diagnosis and prognosis in neuroimaging for dementia; a systematic review

Contact Info

Product

Resources

About