6,463 research outputs found
Deep Learning for Multiclass Classification, Predictive Modeling and Segmentation of Disease Prone Regions in Alzheimer’s Disease
One of the challenges facing accurate diagnosis and prognosis of Alzheimer’s Disease (AD) is identifying the subtle changes that define the early onset of the disease. This dissertation investigates three of the main challenges confronted when such subtle changes are to be identified in the most meaningful way. These are (1) the missing data challenge, (2) longitudinal modeling of disease progression, and (3) the segmentation and volumetric calculation of disease-prone brain areas in medical images. The scarcity of sufficient data compounded by the missing data challenge in many longitudinal samples exacerbates the problem as we seek statistical meaningfulness in multiclass classification and regression analysis. Although there are many participants in the AD Neuroimaging Initiative (ADNI) study, many of the observations have a lot of missing features which often lead to the exclusion of potentially valuable data points that could add significant meaning in many ongoing experiments. Motivated by the necessity of examining all participants, even those with missing tests or imaging modalities, multiple techniques of handling missing data in this domain have been explored. Specific attention was drawn to the Gradient Boosting (GB) algorithm which has an inherent capability of addressing missing values. Prior to applying state-of-the-art classifiers such as Support Vector Machine (SVM) and Random Forest (RF), the impact of imputing data in common datasets with numerical techniques has been also investigated and compared with the GB algorithm. Furthermore, to discriminate AD subjects from healthy control individuals, and Mild Cognitive Impairment (MCI), longitudinal multimodal heterogeneous data was modeled using recurring neural networks (RNNs). In the segmentation and volumetric calculation challenge, this dissertation places its focus on one of the most relevant disease-prone areas in many neurological and neurodegenerative diseases, the hippocampus region. Changes in hippocampus shape and volume are considered significant biomarkers for AD diagnosis and prognosis. Thus, a two-stage model based on integrating the Vision Transformer and Convolutional Neural Network (CNN) is developed to automatically locate, segment, and estimate the hippocampus volume from the brain 3D MRI. The proposed architecture was trained and tested on a dataset containing 195 brain MRIs from the 2019 Medical Segmentation Decathlon Challenge against the manually segmented regions provided therein and was deployed on 326 MRI from our own data collected through Mount Sinai Medical Center as part of the 1Florida Alzheimer Disease Research Center (ADRC)
Ensemble deep learning: A review
Ensemble learning combines several individual models to obtain better
generalization performance. Currently, deep learning models with multilayer
processing architecture is showing better performance as compared to the
shallow or traditional classification models. Deep ensemble learning models
combine the advantages of both the deep learning models as well as the ensemble
learning such that the final model has better generalization performance. This
paper reviews the state-of-art deep ensemble models and hence serves as an
extensive summary for the researchers. The ensemble models are broadly
categorised into ensemble models like bagging, boosting and stacking, negative
correlation based deep ensemble models, explicit/implicit ensembles,
homogeneous /heterogeneous ensemble, decision fusion strategies, unsupervised,
semi-supervised, reinforcement learning and online/incremental, multilabel
based deep ensemble models. Application of deep ensemble models in different
domains is also briefly discussed. Finally, we conclude this paper with some
future recommendations and research directions
Multilevel Feature Representation of FDG-PET Brain Images for Diagnosing Alzheimer's Disease
International audienc
- …