534 research outputs found
Spectral Graph Convolutions for Population-based Disease Prediction
Exploiting the wealth of imaging and non-imaging information for disease
prediction tasks requires models capable of representing, at the same time,
individual features as well as data associations between subjects from
potentially large populations. Graphs provide a natural framework for such
tasks, yet previous graph-based approaches focus on pairwise similarities
without modelling the subjects' individual characteristics and features. On the
other hand, relying solely on subject-specific imaging feature vectors fails to
model the interaction and similarity between subjects, which can reduce
performance. In this paper, we introduce the novel concept of Graph
Convolutional Networks (GCN) for brain analysis in populations, combining
imaging and non-imaging data. We represent populations as a sparse graph where
its vertices are associated with image-based feature vectors and the edges
encode phenotypic information. This structure was used to train a GCN model on
partially labelled graphs, aiming to infer the classes of unlabelled nodes from
the node features and pairwise associations between subjects. We demonstrate
the potential of the method on the challenging ADNI and ABIDE databases, as a
proof of concept of the benefit from integrating contextual information in
classification tasks. This has a clear impact on the quality of the
predictions, leading to 69.5% accuracy for ABIDE (outperforming the current
state of the art of 66.8%) and 77% for ADNI for prediction of MCI conversion,
significantly outperforming standard linear classifiers where only individual
features are considered.Comment: International Conference on Medical Image Computing and
Computer-Assisted Interventions (MICCAI) 201
Sawtooth period changes with mode conversion current drive on Alcator C-Mod
DEFC0299ER54512. Reproduction, translation, publication, use and disposal, in whole or in part, by or for the United States government is permitted. Submitted for publication to Plasma Physics and Controlled Fusion. Sawtooth period changes with mode conversion current drive on Alcator C-Mo
GAN-based multiple adjacent brain MRI slice reconstruction for unsupervised alzheimer’s disease diagnosis
Unsupervised learning can discover various unseen diseases, relying on
large-scale unannotated medical images of healthy subjects. Towards this,
unsupervised methods reconstruct a single medical image to detect outliers
either in the learned feature space or from high reconstruction loss. However,
without considering continuity between multiple adjacent slices, they cannot
directly discriminate diseases composed of the accumulation of subtle
anatomical anomalies, such as Alzheimer's Disease (AD). Moreover, no study has
shown how unsupervised anomaly detection is associated with disease stages.
Therefore, we propose a two-step method using Generative Adversarial
Network-based multiple adjacent brain MRI slice reconstruction to detect AD at
various stages: (Reconstruction) Wasserstein loss with Gradient Penalty + L1
loss---trained on 3 healthy slices to reconstruct the next 3
ones---reconstructs unseen healthy/AD cases; (Diagnosis) Average/Maximum loss
(e.g., L2 loss) per scan discriminates them, comparing the reconstructed/ground
truth images. The results show that we can reliably detect AD at a very early
stage with Area Under the Curve (AUC) 0.780 while also detecting AD at a late
stage much more accurately with AUC 0.917; since our method is fully
unsupervised, it should also discover and alert any anomalies including rare
disease.Comment: 10 pages, 4 figures, Accepted to Lecture Notes in Bioinformatics
(LNBI) as a volume in the Springer serie
Edge-variational Graph Convolutional Networks for Uncertainty-aware Disease Prediction
There is a rising need for computational models that can complementarily
leverage data of different modalities while investigating associations between
subjects for population-based disease analysis. Despite the success of
convolutional neural networks in representation learning for imaging data, it
is still a very challenging task. In this paper, we propose a generalizable
framework that can automatically integrate imaging data with non-imaging data
in populations for uncertainty-aware disease prediction. At its core is a
learnable adaptive population graph with variational edges, which we
mathematically prove that it is optimizable in conjunction with graph
convolutional neural networks. To estimate the predictive uncertainty related
to the graph topology, we propose the novel concept of Monte-Carlo edge
dropout. Experimental results on four databases show that our method can
consistently and significantly improve the diagnostic accuracy for Autism
spectrum disorder, Alzheimer's disease, and ocular diseases, indicating its
generalizability in leveraging multimodal data for computer-aided diagnosis.Comment: Accepted to MICCAI 202
Monitoramento das águas subterráneas adjacentes ao aterro sanitário de Taubaté(SP): primeiros resultados
36 observation wells, varying from 2 to 5 meters in depth to water, were installed over a 20,000 m² area downgradient from the Taubate sanitary landílll as part of a project to monitor groundwater quality at the site. In 1984, the first year of the study, water leveis in the wells were measured monthly and water quality samples were collected and analyzed every 3 months. The chemical analyses results showed an increase in the overall mineralization of the groundwater in the vicinity of the landfill, particularly for chloride, soium bicarbonate, potassium, magnesium and ammonium ions. Good correlations were demonstrated between specific conductance and total dissolve d solids (TDS), which consiste d principally of chloride and sodium ions. For the geohydrologic environment studied, the best indicators of landfill pollution were: specific conductance, sodium, chloride and ammonium ionsForam instalados 36 poços de observação, de 2 a 5m de profundidade numa área de 20.000 m² a jusante do aterro sanitário de Taubaté a fim de se monitorar a qualidade das águas subterrâneas no local. Em 1984, primeiro ano do estudo, o nível de água nos poços foi medido mensalmente e foram coletadas e analisadas amostras de água de cada poço trimestralmente. Os resultados das análises químicas mostraram que a proximidade do depósito de lixo provoca o aumento da mineralização total da água subterrânea, e em particular das concentrações dos íons cloreto, sódio, bicarbonato, potássio, magnésio e amônio. Foram evidenciadas boas correlações lineares entre a condutividade e as concentrações de sólidos totais dissolvidos - de cloretos e de sódio. No ambiente hidrogeológico estudado, a condutividade, o sódio, o cloreto e o nitrogênio amordaçai constituem os indicadores da poluição pelo lix
Off-Axis Nulling Transfer Function Measurement: A First Assessment
We want to study a polychromatic inverse problem method with nulling interferometers to obtain information on the structures of the exozodiacal light. For this reason, during the first semester of 2013, thanks to the support of the consortium PERSEE, we launched a campaign of laboratory measurements with the nulling interferometric test bench PERSEE, operating with 9 spectral channels between J and K bands. Our objective is to characterise the transfer function, i.e. the map of the null as a function of wavelength for an off-axis source, the null being optimised on the central source or on the source photocenter. We were able to reach on-axis null depths better than 10(exp 4). This work is part of a broader project aiming at creating a simulator of a nulling interferometer in which typical noises of a real instrument are introduced. We present here our first results
SPHERE: the exoplanet imager for the Very Large Telescope
Observations of circumstellar environments to look for the direct signal of
exoplanets and the scattered light from disks has significant instrumental
implications. In the past 15 years, major developments in adaptive optics,
coronagraphy, optical manufacturing, wavefront sensing and data processing,
together with a consistent global system analysis have enabled a new generation
of high-contrast imagers and spectrographs on large ground-based telescopes
with much better performance. One of the most productive is the
Spectro-Polarimetic High contrast imager for Exoplanets REsearch (SPHERE)
designed and built for the ESO Very Large Telescope (VLT) in Chile. SPHERE
includes an extreme adaptive optics system, a highly stable common path
interface, several types of coronagraphs and three science instruments. Two of
them, the Integral Field Spectrograph (IFS) and the Infra-Red Dual-band Imager
and Spectrograph (IRDIS), are designed to efficiently cover the near-infrared
(NIR) range in a single observation for efficient young planet search. The
third one, ZIMPOL, is designed for visible (VIR) polarimetric observation to
look for the reflected light of exoplanets and the light scattered by debris
disks. This suite of three science instruments enables to study circumstellar
environments at unprecedented angular resolution both in the visible and the
near-infrared. In this work, we present the complete instrument and its on-sky
performance after 4 years of operations at the VLT.Comment: Final version accepted for publication in A&
Prediction of Thrombectomy Functional Outcomes using Multimodal Data
Recent randomised clinical trials have shown that patients with ischaemic
stroke {due to occlusion of a large intracranial blood vessel} benefit from
endovascular thrombectomy. However, predicting outcome of treatment in an
individual patient remains a challenge. We propose a novel deep learning
approach to directly exploit multimodal data (clinical metadata information,
imaging data, and imaging biomarkers extracted from images) to estimate the
success of endovascular treatment. We incorporate an attention mechanism in our
architecture to model global feature inter-dependencies, both channel-wise and
spatially. We perform comparative experiments using unimodal and multimodal
data, to predict functional outcome (modified Rankin Scale score, mRS) and
achieve 0.75 AUC for dichotomised mRS scores and 0.35 classification accuracy
for individual mRS scores.Comment: Accepted at Medical Image Understanding and Analysis (MIUA) 202
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