Deep learning for radiotherapy outcome prediction using dose data–a review
Artificial intelligence, and in particular deep learning using convolutional neural networks,
has been used extensively for image classification and segmentation, including on medical …
has been used extensively for image classification and segmentation, including on medical …
Hierarchical fully convolutional network for joint atrophy localization and Alzheimer's disease diagnosis using structural MRI
Structural magnetic resonance imaging (sMRI) has been widely used for computer-aided
diagnosis of neurodegenerative disorders, eg, Alzheimer's disease (AD), due to its …
diagnosis of neurodegenerative disorders, eg, Alzheimer's disease (AD), due to its …
An explainable 3D residual self-attention deep neural network for joint atrophy localization and Alzheimer's disease diagnosis using structural MRI
Computer-aided early diagnosis of Alzheimer's disease (AD) and its prodromal form mild
cognitive impairment (MCI) based on structure Magnetic Resonance Imaging (sMRI) has …
cognitive impairment (MCI) based on structure Magnetic Resonance Imaging (sMRI) has …
A deep learning model for early prediction of Alzheimer's disease dementia based on hippocampal magnetic resonance imaging data
Introduction It is challenging at baseline to predict when and which individuals who meet
criteria for mild cognitive impairment (MCI) will ultimately progress to Alzheimer's disease …
criteria for mild cognitive impairment (MCI) will ultimately progress to Alzheimer's disease …
[HTML][HTML] Deep learning in neuroimaging data analysis: Applications, challenges, and solutions
LK Avberšek, G Repovš - Frontiers in neuroimaging, 2022 - frontiersin.org
Methods for the analysis of neuroimaging data have advanced significantly since the
beginning of neuroscience as a scientific discipline. Today, sophisticated statistical …
beginning of neuroscience as a scientific discipline. Today, sophisticated statistical …
3D CNN-based classification using sMRI and MD-DTI images for Alzheimer disease studies
Computer-aided early diagnosis of Alzheimers Disease (AD) and its prodromal form, Mild
Cognitive Impairment (MCI), has been the subject of extensive research in recent years …
Cognitive Impairment (MCI), has been the subject of extensive research in recent years …
Weakly supervised deep learning for brain disease prognosis using MRI and incomplete clinical scores
As a hot topic in brain disease prognosis, predicting clinical measures of subjects based on
brain magnetic resonance imaging (MRI) data helps to assess the stage of pathology and …
brain magnetic resonance imaging (MRI) data helps to assess the stage of pathology and …
Attention-guided hybrid network for dementia diagnosis with structural MR images
Deep-learning methods (especially convolutional neural networks) using structural magnetic
resonance imaging (sMRI) data have been successfully applied to computer-aided …
resonance imaging (sMRI) data have been successfully applied to computer-aided …
Ulcer severity grading in video capsule images of patients with Crohn's disease: an ordinal neural network solution
Background and Aims Capsule endoscopy (CE) is an important modality for diagnosis and
follow-up of Crohn's disease (CD). The severity of ulcers at endoscopy is significant for …
follow-up of Crohn's disease (CD). The severity of ulcers at endoscopy is significant for …
[Retracted] On Improved 3D‐CNN‐Based Binary and Multiclass Classification of Alzheimer's Disease Using Neuroimaging Modalities and Data Augmentation …
Alzheimer's disease (AD) is an irreversible illness of the brain impacting the functional and
daily activities of elderly population worldwide. Neuroimaging sensory systems such as …
daily activities of elderly population worldwide. Neuroimaging sensory systems such as …