Deep learning for radiotherapy outcome prediction using dose data–a review

AL Appelt, B Elhaminia, A Gooya, A Gilbert, M Nix - Clinical Oncology, 2022 - Elsevier
Artificial intelligence, and in particular deep learning using convolutional neural networks,
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

C Lian, M Liu, J Zhang, D Shen - IEEE transactions on pattern …, 2018 - ieeexplore.ieee.org
Structural magnetic resonance imaging (sMRI) has been widely used for computer-aided
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

X Zhang, L Han, W Zhu, L Sun… - IEEE journal of …, 2021 - ieeexplore.ieee.org
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 …

A deep learning model for early prediction of Alzheimer's disease dementia based on hippocampal magnetic resonance imaging data

H Li, M Habes, DA Wolk, Y Fan… - Alzheimer's & …, 2019 - Elsevier
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 …

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 …

3D CNN-based classification using sMRI and MD-DTI images for Alzheimer disease studies

A Khvostikov, K Aderghal, J Benois-Pineau… - arXiv preprint arXiv …, 2018 - arxiv.org
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 …

Attention-guided hybrid network for dementia diagnosis with structural MR images

C Lian, M Liu, Y Pan, D Shen - IEEE transactions on …, 2020 - ieeexplore.ieee.org
Deep-learning methods (especially convolutional neural networks) using structural magnetic
resonance imaging (sMRI) data have been successfully applied to computer-aided …

Enhanced Harris hawks optimization-based fuzzy k-nearest neighbor algorithm for diagnosis of Alzheimer's disease

Q Zhang, J Sheng, Q Zhang, L Wang, Z Yang… - Computers in Biology …, 2023 - Elsevier
In order to stop deterioration and give patients with Alzheimer's disease (AD) early therapy, it
is crucial to correctly diagnose AD and its early stage, mild cognitive impairment (MCI). A …

Weakly supervised deep learning for brain disease prognosis using MRI and incomplete clinical scores

M Liu, J Zhang, C Lian, D Shen - IEEE transactions on …, 2019 - ieeexplore.ieee.org
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 …

Ulcer severity grading in video capsule images of patients with Crohn's disease: an ordinal neural network solution

Y Barash, L Azaria, S Soffer, RM Yehuda… - Gastrointestinal …, 2021 - Elsevier
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 …