67,501 research outputs found
Bridging structural MRI with cognitive function for individual level classification of early psychosis via deep learning.
Recent efforts have been made to apply machine learning and deep learning approaches to the automated classification of schizophrenia using structural magnetic resonance imaging (sMRI) at the individual level. However, these approaches are less accurate on early psychosis (EP) since there are mild structural brain changes at early stage. As cognitive impairments is one main feature in psychosis, in this study we apply a multi-task deep learning framework using sMRI with inclusion of cognitive assessment to facilitate the classification of patients with EP from healthy individuals.
Unlike previous studies, we used sMRI as the direct input to perform EP classifications and cognitive estimations. The proposed deep learning model does not require time-consuming volumetric or surface based analysis and can provide additionally cognition predictions. Experiments were conducted on an in-house data set with 77 subjects and a public ABCD HCP-EP data set with 164 subjects.
We achieved 74.9 ± 4.3% five-fold cross-validated accuracy and an area under the curve of 71.1 ± 4.1% on EP classification with the inclusion of cognitive estimations.
We reveal the feasibility of automated cognitive estimation using sMRI by deep learning models, and also demonstrate the implicit adoption of cognitive measures as additional information to facilitate EP classifications from healthy controls
Automated Classification of Periodic Variable Stars detected by the Wide-field Infrared Survey Explorer
We describe a methodology to classify periodic variable stars identified
using photometric time-series measurements constructed from the Wide-field
Infrared Survey Explorer (WISE) full-mission single-exposure Source Databases.
This will assist in the future construction of a WISE Variable Source Database
that assigns variables to specific science classes as constrained by the WISE
observing cadence with statistically meaningful classification probabilities.
We have analyzed the WISE light curves of 8273 variable stars identified in
previous optical variability surveys (MACHO, GCVS, and ASAS) and show that
Fourier decomposition techniques can be extended into the mid-IR to assist with
their classification. Combined with other periodic light-curve features, this
sample is then used to train a machine-learned classifier based on the random
forest (RF) method. Consistent with previous classification studies of variable
stars in general, the RF machine-learned classifier is superior to other
methods in terms of accuracy, robustness against outliers, and relative
immunity to features that carry little or redundant class information. For the
three most common classes identified by WISE: Algols, RR Lyrae, and W Ursae
Majoris type variables, we obtain classification efficiencies of 80.7%, 82.7%,
and 84.5% respectively using cross-validation analyses, with 95% confidence
intervals of approximately +/-2%. These accuracies are achieved at purity (or
reliability) levels of 88.5%, 96.2%, and 87.8% respectively, similar to that
achieved in previous automated classification studies of periodic variable
stars.Comment: 48 pages, 17 figures, 1 table, accepted by A
Towards the Automatic Classification of Documents in User-generated Classifications
There is a huge amount of information scattered on the World Wide Web. As the information flow occurs at a high speed in the WWW, there is a need to organize it in the right manner so that a user can access it very easily. Previously the organization of information was generally done manually, by matching the document contents to some pre-defined categories. There are two approaches for this text-based categorization: manual and automatic. In the manual approach, a human expert performs the classification task, and in the second case supervised classifiers are used to automatically classify resources. In a supervised classification, manual interaction is required to create some training data before the automatic classification task takes place. In our new approach, we intend to propose automatic classification of documents through semantic keywords and building the formulas generation by these keywords. Thus we can reduce this human participation by combining the knowledge of a given classification and the knowledge extracted from the data. The main focus of this PhD thesis, supervised by Prof. Fausto Giunchiglia, is the automatic classification of documents into user-generated classifications. The key benefits foreseen from this automatic document classification is not only related to search engines, but also to many other fields like, document organization, text filtering, semantic index managing
Galaxy Zoo Supernovae
This paper presents the first results from a new citizen science project:
Galaxy Zoo Supernovae. This proof of concept project uses members of the public
to identify supernova candidates from the latest generation of wide-field
imaging transient surveys. We describe the Galaxy Zoo Supernovae operations and
scoring model, and demonstrate the effectiveness of this novel method using
imaging data and transients from the Palomar Transient Factory (PTF). We
examine the results collected over the period April-July 2010, during which
nearly 14,000 supernova candidates from PTF were classified by more than 2,500
individuals within a few hours of data collection. We compare the transients
selected by the citizen scientists to those identified by experienced PTF
scanners, and find the agreement to be remarkable - Galaxy Zoo Supernovae
performs comparably to the PTF scanners, and identified as transients 93% of
the ~130 spectroscopically confirmed SNe that PTF located during the trial
period (with no false positive identifications). Further analysis shows that
only a small fraction of the lowest signal-to-noise SN detections (r > 19.5)
are given low scores: Galaxy Zoo Supernovae correctly identifies all SNe with >
8{\sigma} detections in the PTF imaging data. The Galaxy Zoo Supernovae project
has direct applicability to future transient searches such as the Large
Synoptic Survey Telescope, by both rapidly identifying candidate transient
events, and via the training and improvement of existing machine classifier
algorithms.Comment: 13 pages, 10 figures, accepted MNRA
Galaxy Zoo: Reproducing Galaxy Morphologies Via Machine Learning
We present morphological classifications obtained using machine learning for
objects in SDSS DR6 that have been classified by Galaxy Zoo into three classes,
namely early types, spirals and point sources/artifacts. An artificial neural
network is trained on a subset of objects classified by the human eye and we
test whether the machine learning algorithm can reproduce the human
classifications for the rest of the sample. We find that the success of the
neural network in matching the human classifications depends crucially on the
set of input parameters chosen for the machine-learning algorithm. The colours
and parameters associated with profile-fitting are reasonable in separating the
objects into three classes. However, these results are considerably improved
when adding adaptive shape parameters as well as concentration and texture. The
adaptive moments, concentration and texture parameters alone cannot distinguish
between early type galaxies and the point sources/artifacts. Using a set of
twelve parameters, the neural network is able to reproduce the human
classifications to better than 90% for all three morphological classes. We find
that using a training set that is incomplete in magnitude does not degrade our
results given our particular choice of the input parameters to the network. We
conclude that it is promising to use machine- learning algorithms to perform
morphological classification for the next generation of wide-field imaging
surveys and that the Galaxy Zoo catalogue provides an invaluable training set
for such purposes.Comment: 13 Pages, 5 figures, 10 tables. Accepted for publication in MNRAS.
Revised to match accepted version
ICLabel: An automated electroencephalographic independent component classifier, dataset, and website
The electroencephalogram (EEG) provides a non-invasive, minimally
restrictive, and relatively low cost measure of mesoscale brain dynamics with
high temporal resolution. Although signals recorded in parallel by multiple,
near-adjacent EEG scalp electrode channels are highly-correlated and combine
signals from many different sources, biological and non-biological, independent
component analysis (ICA) has been shown to isolate the various source generator
processes underlying those recordings. Independent components (IC) found by ICA
decomposition can be manually inspected, selected, and interpreted, but doing
so requires both time and practice as ICs have no particular order or intrinsic
interpretations and therefore require further study of their properties.
Alternatively, sufficiently-accurate automated IC classifiers can be used to
classify ICs into broad source categories, speeding the analysis of EEG studies
with many subjects and enabling the use of ICA decomposition in near-real-time
applications. While many such classifiers have been proposed recently, this
work presents the ICLabel project comprised of (1) an IC dataset containing
spatiotemporal measures for over 200,000 ICs from more than 6,000 EEG
recordings, (2) a website for collecting crowdsourced IC labels and educating
EEG researchers and practitioners about IC interpretation, and (3) the
automated ICLabel classifier. The classifier improves upon existing methods in
two ways: by improving the accuracy of the computed label estimates and by
enhancing its computational efficiency. The ICLabel classifier outperforms or
performs comparably to the previous best publicly available method for all
measured IC categories while computing those labels ten times faster than that
classifier as shown in a rigorous comparison against all other publicly
available EEG IC classifiers.Comment: Intended for NeuroImage. Updated from version one with minor
editorial and figure change
Automated Protein Structure Classification: A Survey
Classification of proteins based on their structure provides a valuable
resource for studying protein structure, function and evolutionary
relationships. With the rapidly increasing number of known protein structures,
manual and semi-automatic classification is becoming ever more difficult and
prohibitively slow. Therefore, there is a growing need for automated, accurate
and efficient classification methods to generate classification databases or
increase the speed and accuracy of semi-automatic techniques. Recognizing this
need, several automated classification methods have been developed. In this
survey, we overview recent developments in this area. We classify different
methods based on their characteristics and compare their methodology, accuracy
and efficiency. We then present a few open problems and explain future
directions.Comment: 14 pages, Technical Report CSRG-589, University of Toront
- …