Neuroimaging modality fusion in Alzheimer’s classification using convolutional neural networks

Punjabi, Arjun; Martersteck, Adam; Wang, Yanran; Parrish, Todd B.; Katsaggelos, Aggelos K.

doi:10.1371/journal.pone.0225759

Cited by 47 publications

(31 citation statements)

References 43 publications

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“…As a result, recent trends in AD diagnosis include the use of DL-based approaches. DL-based [7,11,12,[19][20][21] studies consider multimodal information for classifying AD and mAD from NC. The studies [7,22,23] use 3D patches from the whole brain to train and test a CNN model.…”

Section: Introductionmentioning

confidence: 99%

Ensemble of ROI-based convolutional neural network classifiers for staging the Alzheimer disease spectrum from magnetic resonance imaging

et al. 2020

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Patches from three orthogonal views of selected cerebral regions can be utilized to learn convolutional neural network (CNN) models for staging the Alzheimer disease (AD) spectrum including preclinical AD, mild cognitive impairment due to AD, and dementia due to AD and normal controls. Hippocampi, amygdalae and insulae were selected from the volumetric analysis of structured magnetic resonance images (MRIs). Three-view patches (TVPs) from these regions were fed to the CNN for training. MRIs were classified with the SoftMax-normalized scores of individual model predictions on TVPs. The significance of each region of interest (ROI) for staging the AD spectrum was evaluated and reported. The results of the ensemble classifier are compared with state-of-the-art methods using the same evaluation metrics. Patch-based ROI ensembles provide comparable diagnostic performance for AD staging. In this work, TVP-based ROI analysis using a CNN provides informative landmarks in cerebral MRIs and may have significance in clinical studies and computer-aided diagnosis system design.

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Section: Introductionmentioning

confidence: 99%

Ensemble of ROI-based convolutional neural network classifiers for staging the Alzheimer disease spectrum from magnetic resonance imaging

et al. 2020

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“…The overwhelming majority of these studies have been focusing on complex and high dimension brain imaging data, especially PET and structural MRI (Jo et al, 2019 ; Ebrahimighahnavieh et al, 2020 ; Gautam and Sharma, 2020 ; Haq et al, 2020 ). Several recent studies have aimed to integrate multimodal imaging to improve classification performance (Suk et al, 2014 ; Lu et al, 2018 ; Huang et al, 2019 ; Punjabi et al, 2019 ; Zhou et al, 2019 ). Deep learning can also help to identify features that are important for disease progression or serve as markers for clinical trials (Ithapu et al, 2015 ).…”

Section: Introductionmentioning

confidence: 99%

A 5-min Cognitive Task With Deep Learning Accurately Detects Early Alzheimer's Disease

Almubark

Chang

Shattuck

et al. 2020

Front. Aging Neurosci.

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Introduction: The goal of this study was to investigate and compare the classification performance of machine learning with behavioral data from standard neuropsychological tests, a cognitive task, or both.Methods: A neuropsychological battery and a simple 5-min cognitive task were administered to eight individuals with mild cognitive impairment (MCI), eight individuals with mild Alzheimer's disease (AD), and 41 demographically match controls (CN). A fully connected multilayer perceptron (MLP) network and four supervised traditional machine learning algorithms were used.Results: Traditional machine learning algorithms achieved similar classification performances with neuropsychological or cognitive data. MLP outperformed traditional algorithms with the cognitive data (either alone or together with neuropsychological data), but not neuropsychological data. In particularly, MLP with a combination of summarized scores from neuropsychological tests and the cognitive task achieved ~90% sensitivity and ~90% specificity. Applying the models to an independent dataset, in which the participants were demographically different from the ones in the main dataset, a high specificity was maintained (100%), but the sensitivity was dropped to 66.67%.Discussion: Deep learning with data from specific cognitive task(s) holds promise for assisting in the early diagnosis of Alzheimer's disease, but future work with a large and diverse sample is necessary to validate and to improve this approach.

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“…A variational autoencoder was used by Choi et al [150] to detect anomalies in PET images, thereby providing a score of abnormality used to identify AD patients. The main imaging modalities used for AD classification are T1w MRI and 18 F-fluorodeoxyglucose PET [147], but others, for example amyloid PET, have been used [151]. Other types of data, such as speech data [152], also bring meaningful information.…”

Section: Disease Recognitionmentioning

confidence: 99%

“…More recent studies used a single neural network that can deal with multimodal data. Punjabi et al [151] combined MRI and PET data for diagnosing Alzheimer's disease and showed that using both modalities increases the diagnostic accuracy. Combining histopathological images, genomic data and clinical data can also improve survival prediction in cancers [196,197].…”

Section: Integration Of Multimodal Datamentioning

confidence: 99%

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Deep learning for brain disorders: from data processing to disease treatment

Burgos

Bottani

Faouzi

et al. 2020

Briefings in Bioinformatics

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In order to reach precision medicine and improve patients’ quality of life, machine learning is increasingly used in medicine. Brain disorders are often complex and heterogeneous, and several modalities such as demographic, clinical, imaging, genetics and environmental data have been studied to improve their understanding. Deep learning, a subpart of machine learning, provides complex algorithms that can learn from such various data. It has become state of the art in numerous fields, including computer vision and natural language processing, and is also growingly applied in medicine. In this article, we review the use of deep learning for brain disorders. More specifically, we identify the main applications, the concerned disorders and the types of architectures and data used. Finally, we provide guidelines to bridge the gap between research studies and clinical routine.

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Neuroimaging modality fusion in Alzheimer’s classification using convolutional neural networks

Cited by 47 publications

References 43 publications

Ensemble of ROI-based convolutional neural network classifiers for staging the Alzheimer disease spectrum from magnetic resonance imaging

Ensemble of ROI-based convolutional neural network classifiers for staging the Alzheimer disease spectrum from magnetic resonance imaging

A 5-min Cognitive Task With Deep Learning Accurately Detects Early Alzheimer's Disease

Deep learning for brain disorders: from data processing to disease treatment

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