Machine learning for filtering out false positive grey matter atrophies in single subject voxel based morphometry: A simulation based study

Külsgaard, Hernán Claudio; Orlando, José Ignacio; Bendersky, Mariana; Princich, Juan Pablo; Manzanera, Luis San Román; Vargas, Alberto; Kochen, Sara Silvia; Larrabide, Ignacio

doi:10.1016/j.jns.2020.117220

Cited by 4 publications

(2 citation statements)

References 29 publications

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“…The final result selects the middle position in the range as the prediction answer. In this study, BLEU-4 and ROUGE-L [ 25 , 26 ] are used to evaluate the final prediction efficiency of the model. BLEU-4 is evaluated by analysing the frequency of the same words between multiple sentences.…”

Section: Construction Of English Multitext Reading Comprehension Modelmentioning

confidence: 99%

[Retracted] Analysis of English Multitext Reading Comprehension Model Based on Deep Belief Neural Network

Tang

2021

Computational Intelligence and Neuroscience

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In order to solve the problems of low accuracy and low efficiency of answer prediction in machine reading comprehension, a multitext English reading comprehension model based on the deep belief neural network is proposed. Firstly, the paragraph selector in the multitext reading comprehension model is constructed. Secondly, the text reader is designed, and the deep belief neural network is introduced to predict the question answering probability. Finally, the popular English dataset of SQuAD is used for test analysis. The final results show that, after the comparative analysis of different learning methods, it is found that the English multitext reading comprehension model has a strong reading comprehension ability. In addition, two evaluation methods are used to score the overall performance of the model, which shows that the overall score of the English multitext reading comprehension model based on the deep confidence neural network is more than 90, and the efficiency will not be reduced because of the change of the number of documents in the dataset. The above results show that the use of the deep belief neural network to improve the probability generation performance of the model can well solve the task of English multitext reading comprehension, effectively reduce the difficulty of machine reading comprehension in multitask reading, and has a good guiding significance for promoting human convenient Internet knowledge acquisition.

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Section: Construction Of English Multitext Reading Comprehension Modelmentioning

confidence: 99%

[Retracted] Analysis of English Multitext Reading Comprehension Model Based on Deep Belief Neural Network

Tang

2021

Computational Intelligence and Neuroscience

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“…The utility of single-subject VBM has been questioned due to a rather high rate of false-positive findings associated with normal variability of single subjects’ neuroanatomy [ 20 , 35 ]. However, the use of VBM to support the diagnosis of neurodegenerative diseases is based on the detection of disease-characteristic atrophy patterns that often comprise a rather large network of non-neighboring brain regions (in AD medial temporal lobe, temporoparietal junction, and posterior cingulate cortex).…”

Section: Discussionmentioning

confidence: 99%

Voxel-based morphometry in single subjects without a scanner-specific normal database using a convolutional neural network

Krüger,

Opfer,

Spies

et al. 2023

Eur Radiol

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Objectives Reliable detection of disease-specific atrophy in individual T1w-MRI by voxel-based morphometry (VBM) requires scanner-specific normal databases (NDB), which often are not available. The aim of this retrospective study was to design, train, and test a deep convolutional neural network (CNN) for single-subject VBM without the need for a NDB (CNN-VBM). Materials and methods The training dataset comprised 8945 T1w scans from 65 different scanners. The gold standard VBM maps were obtained by conventional VBM with a scanner-specific NDB for each of the 65 scanners. CNN-VBM was tested in an independent dataset comprising healthy controls (n = 37) and subjects with Alzheimer’s disease (AD, n = 51) or frontotemporal lobar degeneration (FTLD, n = 30). A scanner-specific NDB for the generation of the gold standard VBM maps was available also for the test set. The technical performance of CNN-VBM was characterized by the Dice coefficient of CNN-VBM maps relative to VBM maps from scanner-specific VBM. For clinical testing, VBM maps were categorized visually according to the clinical diagnoses in the test set by two independent readers, separately for both VBM methods. Results The VBM maps from CNN-VBM were similar to the scanner-specific VBM maps (median Dice coefficient 0.85, interquartile range [0.81, 0.90]). Overall accuracy of the visual categorization of the VBM maps for the detection of AD or FTLD was 89.8% for CNN-VBM and 89.0% for scanner-specific VBM. Conclusion CNN-VBM without NDB provides a similar performance in the detection of AD- and FTLD-specific atrophy as conventional VBM. Clinical relevance statement A deep convolutional neural network for voxel-based morphometry eliminates the need of scanner-specific normal databases without relevant performance loss and, therefore, could pave the way for the widespread clinical use of voxel-based morphometry to support the diagnosis of neurodegenerative diseases. Key Points • The need of normal databases is a barrier for widespread use of voxel-based brain morphometry. • A convolutional neural network achieved a similar performance for detection of atrophy than conventional voxel-based morphometry. • Convolutional neural networks can pave the way for widespread clinical use of voxel-based morphometry. Graphical abstract

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An Effective Connectivity Model Based on Excitation-Inhibition Imbalance to Classify States of the Epileptogenic Network

Collavini,

Ferńandez-Corazza,

Granado

et al. 2024

IFMBE Proceedings

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Machine learning for filtering out false positive grey matter atrophies in single subject voxel based morphometry: A simulation based study

Cited by 4 publications

References 29 publications

[Retracted] Analysis of English Multitext Reading Comprehension Model Based on Deep Belief Neural Network

[Retracted] Analysis of English Multitext Reading Comprehension Model Based on Deep Belief Neural Network

Voxel-based morphometry in single subjects without a scanner-specific normal database using a convolutional neural network

An Effective Connectivity Model Based on Excitation-Inhibition Imbalance to Classify States of the Epileptogenic Network

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