Suchergebnisse - normative deep autoencoder

A generalizable normative deep autoencoder for brain morphological anomaly detection: application to the multi-site StratiBip dataset on bipolar disorder in an external validation framework von Sampaio, Inês Won, Tassi, Emma, Bellani, Marcella, Benedetti, Francesco, Nenadić, Igor, Phillips, Mary L., Piras, Fabrizio, Yatham, Lakshmi, Bianchi, Anna Maria, Brambilla, Paolo, Maggioni, Eleonora

“… ). We used deep autoencoders in an anomaly detection framework, combined for the first time with a confounder removal step that integrates training and external validation …”
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A generalizable normative deep autoencoder for brain morphological anomaly detection: application to the multi-site StratiBip dataset on bipolar disorder in an external validation framework von Sampaio, Inês Won, Tassi, Emma, Bellani, Marcella, Benedetti, Francesco, Nenadic, Igor, Phillips, Mary, Piras, Fabrizio, Yatham, Lakshmi, Bianchi, Anna Maria, Brambilla, Paolo, Maggioni, Eleonora

“… ). We employed deep autoencoders in an anomaly detection framework, combined with a confounder removal step integrating training and external validation …”
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Using deep autoencoders to identify abnormal brain structural patterns in neuropsychiatric disorders: A large‐scale multi‐sample study von Pinaya, Walter H. L., Mechelli, Andrea, Sato, João R.

“… ‐based disorders which aim to overcome these limitations. We used an artificial neural network known as “deep autoencoder …”
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Normative modelling using deep autoencoders: a multi-cohort study on mild cognitive impairment and Alzheimer's disease von Pinaya, Walter H L, Scarpazza, Cristina, Garcia-Dias, Rafael, Vieira, Sandra, Baecker, Lea, Da Costa, Pedro F, Redolfi, Alberto, Frisoni, Giovanni B, Pievani, Michela, Calhoun, Vince D, Sato, João R, Mechelli, Andrea, Alzheimer's Disease Neuroimaging Initiative, Australian Imaging Biomarkers And Lifestyle Flagship Study Of Ageing

“… In this study, we assessed normative models based on deep autoencoders using structural neuroimaging data from patients with Alzheimer's disease (n=206 …”
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Using normative modelling to detect disease progression in mild cognitive impairment and Alzheimer’s disease in a cross-sectional multi-cohort study von Pinaya, Walter H. L., Scarpazza, Cristina, Garcia-Dias, Rafael, Vieira, Sandra, Baecker, Lea, F da Costa, Pedro, Redolfi, Alberto, Frisoni, Giovanni B., Pievani, Michela, Calhoun, Vince D., Sato, João R., Mechelli, Andrea

“… In this study, we assessed normative models based on deep autoencoders using structural neuroimaging data from patients with Alzheimer’s disease (n = 206 …”
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Personalized MRI-based characterization of subcortical anomalies in Ataxia-Telangiectasia using deep-learning von Saini, Catalina, Salazar-Vilches, Cristian, Blanchard, Caroline C.V., Whitehouse, William P., Parra, Denis, Dineen, Rob A., Pszczolkowski, Stefan

“… To characterize basal ganglia abnormalities in A-T using a normative self-supervised deep autoencoder trained on MRI-based diffusion and perfusion features from healthy children …”
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AutoLog: Anomaly detection by deep autoencoding of system logs von Catillo, Marta, Pecchia, Antonio, Villano, Umberto

“… The use of system logs for detecting and troubleshooting anomalies of production systems has been known since the early days of computers. In spite of the …”
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Next-Gen Manhole Monitoring: Autoencoder-Assisted Anomaly Detection von Krishnan, R. Santhana, Gopikumar, S., Muthu, A. Essaki, Raj, J. Relin Francis, Kumari, D. Abitha, Malar, P. Stella Rose

“… To address these challenges, an advanced anomaly detection system is proposed, leveraging deep learning with Autoencoders …”
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Normative Modeling using Multimodal Variational Autoencoders to Identify Abnormal Brain Volume Deviations in Alzheimer's Disease von Kumar, Sayantan, Payne, Philip R O, Sotiras, Aristeidis

“… Existing deep learning based normative models have been applied on only single modality Magnetic Resonance Imaging (MRI) neuroimaging data …”
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Normative Modeling Via Conditional Variational Autoencoder and Adversarial Learning to Identify Brain Dysfunction in Alzheimer's Disease von Wang, Xuetong, Zhou, Rong, Zhao, Kanhao, Leow, Alex, Zhang, Yu, He, Lifang

“… In this study, we propose a novel normative modeling method by combining conditional variational autoencoder with adversarial learning (ACVAE …”
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Novelty Detection using Deep Normative Modeling for IMU-Based Abnormal Movement Monitoring in Parkinson’s Disease and Autism Spectrum Disorders von Mohammadian Rad, Nastaran, Van Laarhoven, Twan, Furlanello, Cesare, Marchiori, Elena

“… In this paper, we propose deep normative modeling as a probabilistic novelty detection method, in which we model the distribution of normal human movements recorded by wearable sensors and try …”
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Deep reinforcement learning and convolutional autoencoders for anomaly detection of congenital inner ear malformations in clinical CT images von López Diez, Paula, Sundgaard, Josefine Vilsbøll, Margeta, Jan, Diab, Khassan, Patou, François, Paulsen, Rasmus R.

“… We propose a framework for inner ear abnormality detection based on deep reinforcement learning for landmark detection which is trained uniquely in normative data …”
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Path-masked Autoencoder Guiding Unsupervised Attribute Graph Node Clustering von DING Xinyu, KONG Bing, CHEN Hongmei, BAO Chongming, ZHOU Lihua

“… obtain the deep potential community information of the network,and can not make sui-table information integration of the features …”
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Comparing factor mixture modeling and conditional Gaussian mixture variational autoencoders for cognitive profile clustering von Orsoni, Matteo, Giovagnoli, Sara, Garofalo, Sara, Mazzoni, Noemi, Spinoso, Matilde, Benassi, Mariagrazia

“… While traditional methods like factor mixture modeling (FMM) have proven robust for identifying latent cognitive structures, recent advancements in deep learning may offer the potential to capture more intricate and complex cognitive patterns …”
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Unsupervised Normative Learning for Quality Assessment on Diffusion MRI von Yu, Jiahao, Wang, Tenglong, He, Yifei, Pan, Yiang, He, Jianzhong, Wu, Ye

“… Recently, a variety of deep learning methods have been proposed to enhance the quality and utility of these acquisitions …”
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Normative Modeling using Multimodal Variational Autoencoders to Identify Abnormal Brain Structural Patterns in Alzheimer Disease von Kumar, Sayantan, Payne, Philip, Sotiras, Aristeidis

“… However, existing deep learning based normative models on multimodal MRI data use unimodal autoencoders with a single encoder and decoder that may fail to capture the relationship between brain …”
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Developing a fair and interpretable representation of the clock drawing test for mitigating low education and racial bias von Zhang, Jiaqing, Bandyopadhyay, Sabyasachi, Kimmet, Faith, Wittmayer, Jack, Khezeli, Kia, Libon, David J., Price, Catherine C., Rashidi, Parisa

“… In this study, we represented clock drawings by a priorly published 10-dimensional deep learning feature set trained on publicly available data from the National Health and Aging Trends Study (NHATS …”
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An Enhanced Conditional Variational Autoencoder-Based Normative Model for Neuroimaging Analysis von Ho, Mai Phuong, Song, Yang, Sachdev, Perminder Singh, Jiang, Jiyang, Wen, Wei

“… Normative modelling in neuroimaging provides a powerful framework for quantifying individual deviations from expected brain measures as a function of relevant covariates …”
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Distribution-based detection of radiographic changes in pneumonia patterns: A COVID-19 case study von C. Pereira, Sofia, Rocha, Joana, Campilho, Aurélio, Mendonça, Ana Maria

“… This study proposes a novel perspective that conceptualizes COVID-19 pneumonia as a deviation from a normative distribution of typical pneumonia patterns …”
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Normative Modeling via Conditional Variational Autoencoder and Adversarial Learning to Identify Brain Dysfunction in Alzheimer's Disease von Wang, Xuetong, Zhao, Kanhao, Zhou, Rong, Leow, Alex, Osorio, Ricardo, Zhang, Yu, He, Lifang

“… In this study, we propose a novel normative modeling method by combining conditional variational autoencoder with adversarial learning (ACVAE …”
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