Machine learning and deep learning approaches for brain disease diagnosis: principles and recent advances
… Here, the total volume of the brain and the estimation of the … SVM and logistic regression
(LR) ML classifiers were applied … learning model was proposed in [79] to define a connection …
(LR) ML classifiers were applied … learning model was proposed in [79] to define a connection …
Brain tumor segmentation and survival prediction using multimodal MRI scans with deep learning
L Sun, S Zhang, H Chen, L Luo - Frontiers in neuroscience, 2019 - frontiersin.org
… research, as it can be used for grade assessment of gliomas … forest regression model is
used to fit the training data and … Generally, they differ in model depth, filter number, connection …
used to fit the training data and … Generally, they differ in model depth, filter number, connection …
Brain age and other bodily 'ages': implications for neuropsychiatry
… regression model, whereby having an older-appearing brain … not unequivocal evidence of
no relationship, interestingly we … An age estimation method using brain local features for T1-…
no relationship, interestingly we … An age estimation method using brain local features for T1-…
Multi-channel 3D deep feature learning for survival time prediction of brain tumor patients using multi-modal neuroimages
… of whole-brain image) can be mapped to fit the target estimates (long/… without considering
the relationship between different … or deep-learning-based regression model in our future work. …
the relationship between different … or deep-learning-based regression model in our future work. …
A survey on deep learning for neuroimaging-based brain disorder analysis
… used this model to estimate total and regional neuroanatomical … In the practical diagnosis of
brain disorder, it shows great … the spatial relationship of the diseased brain regions and other …
brain disorder, it shows great … the spatial relationship of the diseased brain regions and other …
A survey on deep learning-based non-invasive brain signals: recent advances and new frontiers
… Then, we provide the frontiers of applying deep learning for non-invasive brain signals …
the deep learning-powered brain signal studies, we report the potential real-world applications …
the deep learning-powered brain signal studies, we report the potential real-world applications …
Bone age assessment with various machine learning techniques: a systematic literature review and meta-analysis
… radiographs automatically, but computes the Bone Age itself using a series of regressions. …
that in relation to using an automated solution to chronological age identification, the use of …
that in relation to using an automated solution to chronological age identification, the use of …
Deep neural network-estimated electrocardiographic age as a mortality predictor
EM Lima, AH Ribeiro, GMM Paixão, MH Ribeiro… - Nature …, 2021 - nature.com
… We perform regression analyses that use the ECG-age as an … in a dose-response relationship
22 , we hypothesize that the … rule based on ECG, brain natriuretic peptide (BNP) levels, …
22 , we hypothesize that the … rule based on ECG, brain natriuretic peptide (BNP) levels, …
A deep learning model for early prediction of Alzheimer's disease dementia based on hippocampal magnetic resonance imaging data
… 2 convolutional layers with a direct connection between its input and output. The … estimated
by the deep AD/NC classification model and baseline clinical variables using Cox regression. …
by the deep AD/NC classification model and baseline clinical variables using Cox regression. …
Prediction of Alzheimer's disease based on deep neural network by integrating gene expression and DNA methylation dataset
… omics data for brain areas such as prefrontal cortex. Therefore, … ReLU is used as an activation
function and a softmax regression … explanation and connection to the mechanism study. …
function and a softmax regression … explanation and connection to the mechanism study. …