Advances in data preprocessing for biomedical data fusion: An overview of the methods, challenges, and prospects
Due to the proliferation of biomedical imaging modalities, such as Photoacoustic
Tomography, Computed Tomography (CT), Optical Microscopy and Tomography, etc …
Tomography, Computed Tomography (CT), Optical Microscopy and Tomography, etc …
A review on neuroimaging-based classification studies and associated feature extraction methods for Alzheimer's disease and its prodromal stages
Neuroimaging has made it possible to measure pathological brain changes associated with
Alzheimer's disease (AD) in vivo. Over the past decade, these measures have been …
Alzheimer's disease (AD) in vivo. Over the past decade, these measures have been …
Generalizable deep learning model for early Alzheimer's disease detection from structural MRIs
Early diagnosis of Alzheimer's disease plays a pivotal role in patient care and clinical trials.
In this study, we have developed a new approach based on 3D deep convolutional neural …
In this study, we have developed a new approach based on 3D deep convolutional neural …
Single subject prediction of brain disorders in neuroimaging: Promises and pitfalls
Neuroimaging-based single subject prediction of brain disorders has gained increasing
attention in recent years. Using a variety of neuroimaging modalities such as structural …
attention in recent years. Using a variety of neuroimaging modalities such as structural …
Landmark-based deep multi-instance learning for brain disease diagnosis
Abstract In conventional Magnetic Resonance (MR) image based methods, two stages are
often involved to capture brain structural information for disease diagnosis, ie, 1) manually …
often involved to capture brain structural information for disease diagnosis, ie, 1) manually …
The effect of machine learning regression algorithms and sample size on individualized behavioral prediction with functional connectivity features
Individualized behavioral/cognitive prediction using machine learning (ML) regression
approaches is becoming increasingly applied. The specific ML regression algorithm and …
approaches is becoming increasingly applied. The specific ML regression algorithm and …
Deep convolution neural network based system for early diagnosis of Alzheimer's disease
RR Janghel, YK Rathore - Irbm, 2021 - Elsevier
Abstract Objectives Alzheimer's Disease (AD) is the most general type of dementia. In all
leading countries, it is one of the primary reasons of death in senior citizens. Currently, it is …
leading countries, it is one of the primary reasons of death in senior citizens. Currently, it is …
Using support vector machine to identify imaging biomarkers of neurological and psychiatric disease: a critical review
G Orru, W Pettersson-Yeo, AF Marquand… - Neuroscience & …, 2012 - Elsevier
Standard univariate analysis of neuroimaging data has revealed a host of neuroanatomical
and functional differences between healthy individuals and patients suffering a wide range …
and functional differences between healthy individuals and patients suffering a wide range …
A review of feature reduction techniques in neuroimaging
B Mwangi, TS Tian, JC Soares - Neuroinformatics, 2014 - Springer
Abstract Machine learning techniques are increasingly being used in making relevant
predictions and inferences on individual subjects neuroimaging scan data. Previous studies …
predictions and inferences on individual subjects neuroimaging scan data. Previous studies …
Radiological images and machine learning: trends, perspectives, and prospects
The application of machine learning to radiological images is an increasingly active
research area that is expected to grow in the next five to ten years. Recent advances in …
research area that is expected to grow in the next five to ten years. Recent advances in …