137 research outputs found
Robust Ensemble Classification Methodology for I123-Ioflupane SPECT Images and Multiple Heterogeneous Biomarkers in the Diagnosis of Parkinsonâs Disease
In last years, several approaches to develop an effective Computer-Aided-Diagnosis
(CAD) system for Parkinsonâs Disease (PD) have been proposed. Most of these methods
have focused almost exclusively on brain images through the use of Machine-Learning
algorithms suitable to characterize structural or functional patterns. Those patterns
provide enough information about the status and/or the progression at intermediate
and advanced stages of Parkinsonâs Disease. Nevertheless this information could be
insufficient at early stages of the pathology. The Parkinsonâs ProgressionMarkers Initiative
(PPMI) database includes neurological images along with multiple biomedical tests.
This information opens up the possibility of comparing different biomarker classification
results. As data come from heterogeneous sources, it is expected that we could include
some of these biomarkers in order to obtain new information about the pathology. Based
on that idea, this work presents an Ensemble Classification model with Performance
Weighting. This proposal has been tested comparing Healthy Control subjects (HC)
vs. patients with PD (considering both PD and SWEDD labeled subjects as the same
class). This model combines several Support-Vector-Machine (SVM) with linear kernel
classifiers for different biomedical group of testsâincluding CerebroSpinal Fluid (CSF),
RNA, and Serum testsâand pre-processed neuroimages features (Voxels-As-Features
and a list of definedMorphological Features) fromPPMI database subjects. The proposed
methodology makes use of all data sources and selects the most discriminant features
(mainly from neuroimages). Using this performance-weighted ensemble classification
model, classification results up to 96% were obtained.This work was supported by the MINECO/FEDER under
the TEC2015-64718-R project and the Ministry of Economy,
Innovation, Science and Employment of the Junta de AndalucĂa
under the Excellence Project P11-TIC-7103
Nonlinear Weighting Ensemble Learning Model to Diagnose Parkinson's Disease Using Multimodal Data
This work was supported by the FEDER/Junta deAndalucia-Consejeria de Transformacion Economica, Industria, Conocimiento y Universidades/Proyecto (B-TIC-586-UGR20); the MCIN/AEI/10.13039/501100011033/ and FEDER \Una manerade hacer Europa" under the RTI2018-098913-B100 project, by the Consejeria de Economia, Innovacion,Ciencia y Empleo (Junta de Andalucia) and FEDER under CV20-45250, A-TIC-080-UGR18 and P20-00525 projects. Grant by F.J.M.M. RYC2021-030875-I funded by MCIN/AEI/10.13039/501100011033 and European Union NextGenerationEU/PRTR. Work by D.C.B. is supported by the MCIN/AEI/FJC2021-048082-I Juan de la Cierva Formacion'. Work by J.E.A. is supported by Next Generation EU Fund through a Margarita Salas Grant, and work by C.J.M. is supported by Ministerio de Universidades under the FPU18/04902 grant.Parkinson's Disease (PD) is the second most prevalent neurodegenerative disorder among adults. Although its triggers are still not clear, they may be due to a combination of different types of biomarkers measured through medical imaging, metabolomics, proteomics or genetics, among others. In this context, we have proposed a Computer-Aided Diagnosis (CAD) system that combines structural and functional imaging data from subjects in Parkinson's Progression Markers Initiative dataset by means of an Ensemble Learning methodology trained to identify and penalize input sources with low classification rates and/or high-variability. This proposal improves results published in recent years and provides an accurate solution not only from the point of view of image preprocessing (including a comparison between different intensity preservation techniques), but also in terms of dimensionality reduction methods (Isomap). In addition, we have also introduced a bagging classification schema for scenarios with unbalanced data.As shown by our results, the CAD proposal is able to detect PD with 96.48% of balanced accuracy, and opens up the possibility of combining any number of input data sources relevant for PD.FEDER/Junta deAndalucia-Consejeria de Transformacion Economica, Industria, Conocimiento y Universidades/Proyecto
B-TIC-586-UGR20MCIN/AEI
P20-00525FEDER \Una manerade hacer Europa
RYC2021-030875-IJunta de AndaluciaEuropean Union (EU)
Spanish Government
RTI2018-098913-B100,
CV20-45250,
A-TIC-080-UGR18European Union (EU)Juan de la Cierva FormacionNext Generation EU Fund through a Margarita Salas GrantMinisterio de Universidades
FPU18/0490
Quantifying inter-hemispheric differences in Parkinsonâs Disease using siamese networks
Classification of medical imaging is one of the most popular application of intelligent systems. A crucial step is to find the features that are relevant for the subsequent classification. One possibility is to compute features derived from the morphology of the target region in order to check its role in the pathology under study. It is also possible to extract relevant features to evaluate the similarity between different regions, in addition to compute morphology-related measures. However, it can be much more useful to model the differences between regions. In this paper, we propose a method based on the principles of siamese neural networks to extract informative features from differences between two brain regions. The output of this network generates a latent space that characterizes differences between the two hemispheres. This output vector is then fed into a linear SVM classifier. The usefulness of this method has been assessed with images from the Parkinsonâs Progression Markers Initiative, demonstrating that differences between the dopaminergic regions of both hemispheres lead to a high performance when classifying controls vs Parkinsonâs disease patients
Ensemble of random forests One vs. Rest classifiers for MCI and AD prediction using ANOVA cortical and subcortical feature selection and partial least squares.
Background: Alzheimerâs disease (AD) is the most common cause of dementia in the elderly and affects approximately 30 million individuals worldwide. Mild cognitive impairment (MCI) is very frequently a prodromal phase of AD, and existing studies have suggested that people with MCI tend to progress to AD at a rate of about 10 % to 15 % per year. However, the ability of clinicians and machine learning systems to predict AD based on MRI biomarkers at an early stage is still a challenging problem that can have a great impact in improving treatments. Method: The proposed system, developed by the SiPBA-UGR team for this challenge, is based on feature standardization, ANOVA feature selection, partial least squares feature dimension reduction and an ensemble of one vs. rest random forest classifiers. With the aim of improving its performance when discriminating healthy controls (HC) from MCI, a second binary classification level was introduced that reconsiders the HC and MCI predictions of the first level. Results: The system was trained and evaluated on an ADNI datasets that consist of T1-weighted MRI morphological measurements from HC, stable MCI, converter MCI and AD subjects. The proposed system yields a 56.25 % classification score on the test subset which consists of 160 real subjects. Comparison with Existing Method(s): The classifier yielded the best performance when compared to: i) One vs. One (OvO), One vs. Rest (OvR) and error correcting output codes (ECOC) as strategies for reducing the multiclass classification task to multiple binary classification problems, ii) support vector machines, gradient boosting classifier and random forest as base binary classifiers, and iii) bagging ensemble learning. Conclusions: A robust method has been proposed for the international challenge on MCI prediction based on MRI data.This work was supported by the MINECO/FEDER under TEC2015-64718-R project, the ConsejerĂa de EconomĂa, Innovacion, Ciencia, y Empleo of the Junta de AndalucĂa under the P11-TIC-7103 Excellence Project and the Salvador de Madariaga Mobility Grants 2017
Optimized One vs One approach in multiclass classification for early Alzheimerâs Disease and Mild Cognitive Impairment diagnosis
The detection of Alzheimerâs Disease in its early stages is crucial for patient care and drugs
development. Motivated by this fact, the neuroimaging community has extensively applied machine learning
techniques to the early diagnosis problem with promising results. The organization of challenges has helped
the community to address different raised problems and to standardize the approaches to the problem. In
this work we use the data from international challenge for automated prediction of MCI from MRI data
to address the multiclass classification problem. We propose a novel multiclass classification approach that
addresses the outlier detection problem, uses pairwise t-test feature selection, project the selected features
onto a Partial-Least-Squares multiclass subspace, and applies one-versus-one error correction output
codes classification. The proposed method yields to an accuracy of 67 % in the multiclass classification,
outperforming all the proposals of the competition.Ministerio de Innovacion y Ciencia Project DEEP-NEUROMAPS
RTI2018-098913-B100Consejeria de Economia, Innovacion, Ciencia, y Empleo of the Junta de Andalucia
A-TIC-080-UGR18 TIC FRONTERAGerman Research Foundation (DFG)
FPU 18/04902United States Department of Health & Human Services
National Institutes of Health (NIH) - USA
NIH National Institute of Neurological Disorders & Stroke (NINDS)
U01 AG024904DOD ADNI Department of Defense
W81XWH-12-2-001
Morphological Characterization of Functional Brain Imaging by Isosurface Analysis in Parkinsonâs Disease.
Finding new biomarkers to model Parkinsonâs Disease (PD) is a challenge not only to help discerning between Healthy Control (HC) subjects and patients with potential PD, but also as a way to measure quantitatively the loss of dopaminergic neurons mainly concentrated at substantia nigra. Within this context, the work presented here tries to provide a set of imaging features based on morphological characteristics extracted from I[123]-Ioflupane SPECT scans to discern between HC and PD participants in a balanced set of 386 scans from Parkinsonâs Progression Markers Initiative (PPMI) database. These features, obtained from isosurfaces of each scan at different intensity levels, have been classified through the use of classical Machine Learning classifiers such as Support-Vector-Machines (SVM) or Našıve Bayesian and compared with the results obtained using a Multi-Layer Perceptron (MLP). The proposed system, based on a Mann-Whitney-Wilcoxon U-Test for feature selection and the SVM approach, yielded a 97.04% balanced accuracy when the performance was evaluated using a 10-fold cross-validation. This proves the reliability of these biomarkers, especially those related to sphericity, center of mass, number of vertices, 2D-projected perimeter or the 2D-projected eccentricity; among others, but including both internal and external isosurfaces.This work was supported by the MINECO/FEDER under the RTI2018-098913-B-I00 and PGC2018- 098813-B-C32 projects and the General Secretariat of Universities, Research and Technology, Junta de AndalucĂa under the Excellence FEDER Project ATIC-117-UGR18
Assisted Diagnosis of Parkinsonism Based on the Striatal Morphology
Parkinsonism is a clinical syndrome characterized by the progressive loss of striatal dopamine. Its diagnosis
is usually corroborated by neuroimaging data such as DaTSCAN neuroimages that allow visualizing
the possible dopamine deficiency. During the last decade, a number of computer systems have been
proposed to automatically analyze DaTSCAN neuroimages, eliminating the subjectivity inherent to the
visual examination of the data. In this work, we propose a computer system based on machine learning
to separate Parkinsonian patients and control subjects using the size and shape of the striatal region,
modeled from DaTSCAN data. First, an algorithm based on adaptative thresholding is used to parcel
the striatum. This region is then divided into two according to the brain hemisphere division and characterized
with 152 measures, extracted from the volume and its three possible 2-dimensional projections.
Afterwards, the Bhattacharyya distance is used to discard the least discriminative measures and, finally,
the neuroimage category is estimated by means of a Support Vector Machine classifier. This method was
evaluated using a dataset with 189 DaTSCAN neuroimages, obtaining an accuracy rate over 94%. This
rate outperforms those obtained by previous approaches that use the intensity of each striatal voxel as
a feature.This work was supported by the MINECO/
FEDER under the TEC2015-64718-R project, the
Ministry of Economy, Innovation, Science and
Employment of the Junta de AndalucŽıa under the
P11-TIC-7103 Excellence Project and the Vicerectorate
of Research and Knowledge Transfer of the
University of Granada
Granger Causality-based Information Fusion Applied to Electrical Measurements from Power Transformers.
In the immediate future, with the increasing presence of electrical vehicles and the large increase in the use of renewable energies, it will be crucial that distribution power networks are managed, supervised and exploited in a similar way as the transmission power systems were in previous decades. To achieve this, the underlying infrastructure requires automated monitoring and digitization, including smart-meters, wide-band communication systems, electronic device based-local controllers, and the Internet of Things. All of these technologies demand a huge amount of data to be curated, processed, interpreted and fused with the aim of real-time predictive control and supervision of medium/low voltage transformer substations. WienerâGranger causality, a statistical notion of causal inference based on Information Fusion could help in the prediction of electrical behaviour arising from common causal dependencies. Originally developed in econometrics, it has successfully been applied to several fields of research such as the neurosciences and is applicable to time series data whereby cause precedes effect. In this paper, we demonstrate the potential of this methodology in the context of power measures for providing theoretical models of low/medium power transformers. Up to our knowledge, the proposed method in this context is the first attempt to build a data-driven power system model based on G-causality. In particular, we analysed directed functional connectivity of electrical measures providing a statistical description of observed responses, and identified the causal structure within data in an exploratory analysis. Pair-wise conditional G-causality of power transformers, their independent evolution in time, and the joint evolution in time and frequency are discussed and analysed in the experimental section.This work was partly supported by the MINECO/ FEDER under the RTI2018-
098913-B100 project. The authors would like to acknowledge the support of
370 CDTI (Centro para el Desarrollo Tecnologico Industrial, Ministerio de Cien cia, Innovacion y Universidades and FEDER, SPAIN) under the PASTORA
project (Ref.: ITC-20181102). and to thank the companies within the PAS TORA consortium: Endesa, Ayesa, OrmazÂŽabal and Ingelectus. We would like
to thank the reviewers for their thoughtful comments and efforts towards im 375 proving our manuscript. Finally, JM Gorriz would like to thank Dr GÂŽomez ExpÂŽosito for his helpful advice and comments
Using XAI in the Clock Drawing Test to reveal the cognitive impairment pattern.
he prevalence of dementia is currently increasing worldwide. This syndrome produces a deteriorationin cognitive function that cannot be reverted. However, an early diagnosis can be crucial for slowing itsprogress. The Clock Drawing Test (CDT) is a widely used paper-and-pencil test for cognitive assessmentin which an individual has to manually draw a clock on a paper. There are a lot of scoring systems forthis test and most of them depend on the subjective assessment of the expert. This study proposes acomputer-aided diagnosis (CAD) system based on artificial intelligence (AI) methods to analyze the CDTand obtain an automatic diagnosis of cognitive impairment (CI). This system employs a preprocessingpipeline in which the clock is detected, centered and binarized to decrease the computational burden.Then, the resulting image is fed into a Convolutional Neural Network (CNN) to identify the informativepatterns within the CDT drawings that are relevant for the assessment of the patientâs cognitive status.Performance is evaluated in a real context where patients with CI and controls have been classified byclinical experts in a balanced sample size of 3282 drawings. The proposed method provides an accuracyof 75.65% in the binary case-control classification task, with an AUC of 0.83. These results are indeedrelevant considering the use of the classic version of the CDT. The large size of the sample suggests thatthe method proposed has a high reliability to be used in clinical contexts and demonstrates the suitabilityof CAD systems in the CDT assessment process. Explainable artificial intelligence (XAI) methods areapplied to identify the most relevant regions during classification. Finding these patterns is extremelyhelpful to understand the brain damage caused by CI. A validation method using resubstitution withupper bound correction in a machine learning approach is also discusseThis work was supported by the MCIN/ AEI/10.13039/501100011033/ and FEDER âUna manera de hacer Europaâ under the RTI2018- 098913-B100 project, by the Consejeria de Economia, Innovacion, Ciencia y Empleo (Junta de An765 dalucia) and FEDER under CV20-45250, A-TIC080-UGR18, B-TIC-586-UGR20 and P20-00525 projects, and by the Ministerio de Universidades under the FPU18/04902 grant given to C. JimenezMesa and the Margarita-Salas grant to J.E. Arco
Search for dark matter produced in association with bottom or top quarks in âs = 13 TeV pp collisions with the ATLAS detector
A search for weakly interacting massive particle dark matter produced in association with bottom or top quarks is presented. Final states containing third-generation quarks and miss- ing transverse momentum are considered. The analysis uses 36.1 fbâ1 of protonâproton collision data recorded by the ATLAS experiment at âs = 13 TeV in 2015 and 2016. No significant excess of events above the estimated backgrounds is observed. The results are in- terpreted in the framework of simplified models of spin-0 dark-matter mediators. For colour- neutral spin-0 mediators produced in association with top quarks and decaying into a pair of dark-matter particles, mediator masses below 50 GeV are excluded assuming a dark-matter candidate mass of 1 GeV and unitary couplings. For scalar and pseudoscalar mediators produced in association with bottom quarks, the search sets limits on the production cross- section of 300 times the predicted rate for mediators with masses between 10 and 50 GeV and assuming a dark-matter mass of 1 GeV and unitary coupling. Constraints on colour- charged scalar simplified models are also presented. Assuming a dark-matter particle mass of 35 GeV, mediator particles with mass below 1.1 TeV are excluded for couplings yielding a dark-matter relic density consistent with measurements
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