7,593 research outputs found
One-Class Classification: Taxonomy of Study and Review of Techniques
One-class classification (OCC) algorithms aim to build classification models
when the negative class is either absent, poorly sampled or not well defined.
This unique situation constrains the learning of efficient classifiers by
defining class boundary just with the knowledge of positive class. The OCC
problem has been considered and applied under many research themes, such as
outlier/novelty detection and concept learning. In this paper we present a
unified view of the general problem of OCC by presenting a taxonomy of study
for OCC problems, which is based on the availability of training data,
algorithms used and the application domains applied. We further delve into each
of the categories of the proposed taxonomy and present a comprehensive
literature review of the OCC algorithms, techniques and methodologies with a
focus on their significance, limitations and applications. We conclude our
paper by discussing some open research problems in the field of OCC and present
our vision for future research.Comment: 24 pages + 11 pages of references, 8 figure
Automatic Autism Spectrum Disorder Detection Using Artificial Intelligence Methods with MRI Neuroimaging: A Review
Autism spectrum disorder (ASD) is a brain condition characterized by diverse
signs and symptoms that appear in early childhood. ASD is also associated with
communication deficits and repetitive behavior in affected individuals. Various
ASD detection methods have been developed, including neuroimaging modalities
and psychological tests. Among these methods, magnetic resonance imaging (MRI)
imaging modalities are of paramount importance to physicians. Clinicians rely
on MRI modalities to diagnose ASD accurately. The MRI modalities are
non-invasive methods that include functional (fMRI) and structural (sMRI)
neuroimaging methods. However, the process of diagnosing ASD with fMRI and sMRI
for specialists is often laborious and time-consuming; therefore, several
computer-aided design systems (CADS) based on artificial intelligence (AI) have
been developed to assist the specialist physicians. Conventional machine
learning (ML) and deep learning (DL) are the most popular schemes of AI used
for diagnosing ASD. This study aims to review the automated detection of ASD
using AI. We review several CADS that have been developed using ML techniques
for the automated diagnosis of ASD using MRI modalities. There has been very
limited work on the use of DL techniques to develop automated diagnostic models
for ASD. A summary of the studies developed using DL is provided in the
appendix. Then, the challenges encountered during the automated diagnosis of
ASD using MRI and AI techniques are described in detail. Additionally, a
graphical comparison of studies using ML and DL to diagnose ASD automatically
is discussed. We conclude by suggesting future approaches to detecting ASDs
using AI techniques and MRI neuroimaging
Histopathological image analysis : a review
Over the past decade, dramatic increases in computational power and improvement in image analysis algorithms have allowed the development of powerful computer-assisted analytical approaches to radiological data. With the recent advent of whole slide digital scanners, tissue histopathology slides can now be digitized and stored in digital image form. Consequently, digitized tissue histopathology has now become amenable to the application of computerized image analysis and machine learning techniques. Analogous to the role of computer-assisted diagnosis (CAD) algorithms in medical imaging to complement the opinion of a radiologist, CAD algorithms have begun to be developed for disease detection, diagnosis, and prognosis prediction to complement the opinion of the pathologist. In this paper, we review the recent state of the art CAD technology for digitized histopathology. This paper also briefly describes the development and application of novel image analysis technology for a few specific histopathology related problems being pursued in the United States and Europe
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
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