4 research outputs found
Representing Alzheimer's Disease Progression via Deep Prototype Tree
For decades, a variety of predictive approaches have been proposed and
evaluated in terms of their predicting capability for Alzheimer's Disease (AD)
and its precursor - mild cognitive impairment (MCI). Most of them focused on
prediction or identification of statistical differences among different
clinical groups or phases (e.g., longitudinal studies). The continuous nature
of AD development and transition states between successive AD related stages
have been overlooked, especially in binary or multi-class classification.
Though a few progression models of AD have been studied recently, they mainly
designed to determine and compare the order of specific biomarkers. How to
effectively predict the individual patient's status within a wide spectrum of
AD progression has been understudied. In this work, we developed a novel
structure learning method to computationally model the continuum of AD
progression as a tree structure. By conducting a novel prototype learning with
a deep manner, we are able to capture intrinsic relations among different
clinical groups as prototypes and represent them in a continuous process for AD
development. We named this method as Deep Prototype Learning and the learned
tree structure as Deep Prototype Tree - DPTree. DPTree represents different
clinical stages as a trajectory reflecting AD progression and predict clinical
status by projecting individuals onto this continuous trajectory. Through this
way, DPTree can not only perform efficient prediction for patients at any
stages of AD development (77.8% accuracy for five groups), but also provide
more information by examining the projecting locations within the entire AD
progression process.Comment: Submitted to Information Processing in Medical Imaging (IPMI) 202
Application of deep learning in detecting neurological disorders from magnetic resonance images: a survey on the detection of Alzheimer’s disease, Parkinson's disease and schizophrenia
Neuroimaging, in particular magnetic resonance imaging (MRI), has been playing an important role in understanding brain functionalities and its disorders during the last couple of decades. These cutting-edge MRI scans, supported by high-performance computational tools and novel ML techniques, have opened up possibilities to unprecedentedly identify neurological disorders. However, similarities in disease phenotypes make it very difficult to detect such disorders accurately from the acquired neuroimaging data. This article critically examines and compares performances of the existing deep learning (DL)-based methods to detect neurological disorders—focusing on Alzheimer’s disease, Parkinson’s disease and schizophrenia—from MRI data acquired using different modalities including functional and structural MRI. The comparative performance analysis of various DL architectures across different disorders and imaging modalities suggests that the Convolutional Neural Network outperforms other methods in detecting neurological disorders. Towards the end, a number of current research challenges are indicated and some possible future research directions are provided