205 research outputs found
Synthetic whole-slide image tile generation with gene expression profile-infused deep generative models
In this work, we propose an approach to generate whole-slide image (WSI) tiles by using deep generative
models infused with matched gene expression profiles. First, we train a variational autoencoder (VAE) that
learns a latent, lower-dimensional representation of multi-tissue gene expression profiles. Then, we use
this representation to infuse generative adversarial networks (GANs) that generate lung and brain cortex
tissue tiles, resulting in a new model that we call RNA-GAN. Tiles generated by RNA-GAN were preferred
by expert pathologists compared with tiles generated using traditional GANs, and in addition, RNA-GAN
needs fewer training epochs to generate high-quality tiles. Finally, RNA-GAN was able to generalize to
gene expression profiles outside of the training set, showing imputation capabilities. A web-based quiz is
available for users to play a game distinguishing real and synthetic tiles: https://rna-gan.stanford.edu/,
and the code for RNA-GAN is available here: https://github.com/gevaertlab/RNA-GAN.Grants PID2021-
128317OB-I00MCIN/AEI/10.13039/501100011033Project
P20-00163, funded by Consejerı´a de Universidad, Investigacio´ n e InnovacioERDF A way of making Europ
Performance comparison between multi‑center histopathology datasets of a weakly‑supervised deep learning model for pancreatic ductal adenocarcinoma detection
Background Pancreatic ductal carcinoma patients have a really poor prognosis given its difficult early detection and
the lack of early symptoms. Digital pathology is routinely used by pathologists to diagnose the disease. However, visually
inspecting the tissue is a time-consuming task, which slows down the diagnostic procedure. With the advances
occurred in the area of artificial intelligence, specifically with deep learning models, and the growing availability of
public histology data, clinical decision support systems are being created. However, the generalization capabilities of
these systems are not always tested, nor the integration of publicly available datasets for pancreatic ductal carcinoma
detection (PDAC).
Methods In this work, we explored the performace of two weakly-supervised deep learning models using the two
more widely available datasets with pancreatic ductal carcinoma histology images, The Cancer Genome Atlas Project
(TCGA) and the Clinical Proteomic Tumor Analysis Consortium (CPTAC). In order to have sufficient training data, the
TCGA dataset was integrated with the Genotype-Tissue Expression (GTEx) project dataset, which contains healthy
pancreatic samples.
Results We showed how the model trained on CPTAC generalizes better than the one trained on the integrated
dataset, obtaining an inter-dataset accuracy of 90.62% ± 2.32 and an outer-dataset accuracy of 92.17% when evaluated
on TCGA + GTEx. Furthermore, we tested the performance on another dataset formed by tissue micro-arrays,
obtaining an accuracy of 98.59%. We showed how the features learned in an integrated dataset do not differentiate
between the classes, but between the datasets, noticing that a stronger normalization might be needed when
creating clinical decision support systems with datasets obtained from different sources. To mitigate this effect, we
proposed to train on the three available datasets, improving the detection performance and generalization capabilities
of a model trained only on TCGA + GTEx and achieving a similar performance to the model trained only on CPTAC.
Conclusions The integration of datasets where both classes are present can mitigate the batch effect present
when integrating datasets, improving the classification performance, and accurately detecting PDAC across different
datasets.Spanish Ministry of Sciences, Innovation and
Universities under Project PID2021-128317OB-I00Junta
de Andalucia P20-0016
Composition Classification of Ultra-High Energy Cosmic Rays
The study of cosmic rays remains as one of the most challenging research fields in Physics.
From the many questions still open in this area, knowledge of the type of primary for each event
remains as one of the most important issues. All of the cosmic rays observatories have been trying
to solve this question for at least six decades, but have not yet succeeded. The main obstacle is the
impossibility of directly detecting high energy primary events, being necessary to use Monte Carlo
models and simulations to characterize generated particles cascades. This work presents the results
attained using a simulated dataset that was provided by the Monte Carlo code CORSIKA, which is
a simulator of high energy particles interactions with the atmosphere, resulting in a cascade of
secondary particles extending for a few kilometers (in diameter) at ground level. Using this simulated
data, a set of machine learning classifiers have been designed and trained, and their computational
cost and effectiveness compared, when classifying the type of primary under ideal measuring
conditions. Additionally, a feature selection algorithm has allowed for identifying the relevance of the
considered features. The results confirm the importance of the electromagnetic-muonic component
separation from signal data measured for the problem. The obtained results are quite encouraging
and open new work lines for future more restrictive simulations.Spanish Ministry of Science, Innovation and Universities
FPA2017-85197-P
RTI2018-101674-B-I00European Union (EU)CENAPAD-SP (Centro Nacional de Processamento de Alto Desempenho em Sao Paulo)
UNICAMP/FINEP - MCTFundacao de Amparo a Pesquisa do Estado de Sao Paulo (FAPESP)National Council for Scientific and Technological Development (CNPq)
2016/19764-9404993/2016-
Intelligent system based on genetic programming for atrial fibrillation classification
This article focuses on the development of intelligent classifiers in the area of biomedicine,
focusing on the problem of diagnosing cardiac diseases based on the electrocardiogram (ECG),
or more precisely, on the differentiation of the types of atrial fibrillations. First of all, we will
study the ECG, and the treatment of the ECG in order to work with it with this specific
pathology. In order to achieve this we will study different ways of elimination, in the best
possible way, of any activity that is not caused by the auriculars. We will study and imitate
the ECG treatment methodologies and the characteristics extracted from the electrocardiograms
that were used by the researchers who obtained the best results in the Physionet Challenge, where
the classification of ECG recordings according to the type of atrial fibrillation (AF) that they
showed, was realized. We will extract a great amount of characteristics, partly those used by these
researchers and additional characteristics that we consider to be important for the distinction
previously mentioned. A new method based on evolutionary algorithms will be used to realize
a selection of the most relevant characteristics and to obtain a classifier that will be capable of
distinguishing the different types of this pathology
Variable Selection in a GPU Cluster Using Delta Test
The work presented in this paper consists in an adaptation
of a Genetic Algorithm (GA) to perform variable selection in an heterogeneous
cluster where the nodes are themselves clusters of GPUs. Due
to this heterogeneity, several mechanisms to perform a load balance will
be discussed as well as the optimization of the fitness function to take
advantage of the GPUs available. The algorithm will be compared with
previous parallel implementations analysing the advantages and disadvantages
of the approach, showing that for large data sets, the proposed
approach is the only one that can provide a solution.Spanish CICYT Project TIN2007-60587 and TEC2008-04920Junta Andalucia
Projects P08-TIC-03674 and P08-TIC03928 and PYR-2010-17 of CEI
BioTIC GENIL (CEB09-0010) of the MICIN
Montaje de los componentes de un servidor para la asignatura del nuevo grado en Ingeniería en Informática: Ingeniería de Servidores
En este trabajo, se presenta una visión general de la nueva asignatura
Ingeniería de Servidores, del nuevo plan de estudios del Grado en Ingeniería
Informática de la Universidad de Granada, así como una nueva metodología
interactiva para que el alumno aprenda a montar un servidor de gama baja. A
través de este aprendizaje práctico, que el Espacio Europeo de Educación
Superior promueve activamente, tratamos de que el alumno descubra cómo
asociar la arquitectura de un servidor con los componentes de los computadores
con los que ellos trabajan a diario
Multiobjective RBFNNs Designer for Function Approximation: An Application for Mineral Reduction
Radial Basis Function Neural Networks (RBFNNs) are well
known because, among other applications, they present a good perfor-
mance when approximating functions. The function approximation prob-
lem arises in the construction of a control system to optimize the process
of the mineral reduction. In order to regulate the temperature of the
ovens and other parameters, it is necessary a module to predict the ¯nal
concentration of mineral that will be obtained from the source materials.
This module can be formed by an RBFNN that predicts the output and
by the algorithm that designs the RBFNN dynamically as more data is
obtained. The design of RBFNNs is a very complex task where many
parameters have to be determined, therefore, a genetic algorithm that
determines all of them has been developed. This algorithm provides sat-
isfactory results since the networks it generates are able to predict quite
precisely the ¯nal concentration of mineral.Spanish
CICYT Project TIN2004-01419European Commission's Research Infrastructures RII3-CT-2003-506079 (HPC-Europa
Integration of RNA-Seq data with heterogeneous microarray data for breast cancer profiling
Background: Nowadays, many public repositories containing large microarray gene expression datasets are
available. However, the problem lies in the fact that microarray technology are less powerful and accurate than more
recent Next Generation Sequencing technologies, such as RNA-Seq. In any case, information from microarrays is
truthful and robust, thus it can be exploited through the integration of microarray data with RNA-Seq data.
Additionally, information extraction and acquisition of large number of samples in RNA-Seq still entails very high costs
in terms of time and computational resources.This paper proposes a new model to find the gene signature of breast
cancer cell lines through the integration of heterogeneous data from different breast cancer datasets, obtained from
microarray and RNA-Seq technologies. Consequently, data integration is expected to provide a more robust statistical
significance to the results obtained. Finally, a classification method is proposed in order to test the robustness of the
Differentially Expressed Genes when unseen data is presented for diagnosis.
Results: The proposed data integration allows analyzing gene expression samples coming from different
technologies. The most significant genes of the whole integrated data were obtained through the intersection of the
three gene sets, corresponding to the identified expressed genes within the microarray data itself, within the RNA-Seq
data itself, and within the integrated data from both technologies. This intersection reveals 98 possible
technology-independent biomarkers. Two different heterogeneous datasets were distinguished for the classification
tasks: a training dataset for gene expression identification and classifier validation, and a test dataset with unseen data
for testing the classifier. Both of them achieved great classification accuracies, therefore confirming the validity of the
obtained set of genes as possible biomarkers for breast cancer. Through a feature selection process, a final small
subset made up by six genes was considered for breast cancer diagnosis.
Conclusions: This work proposes a novel data integration stage in the traditional gene expression analysis pipeline
through the combination of heterogeneous data from microarrays and RNA-Seq technologies. Available samples
have been successfully classified using a subset of six genes obtained by a feature selection method. Consequently, a
new classification and diagnosis tool was built and its performance was validated using previously unseen samples.This work was supported by Project TIN2015-71873-R (Spanish Ministry of
Economy and Competitiveness -MINECO- and the European Regional
Development Fund -ERDF)
Applications of artificial intelligence in dentistry: A comprehensive review
This work was funded by the Spanish Ministry of Sciences, Innovation and Universities under Projects RTI2018-101674-B-I00 and PGC2018-101904-A-100, University of Granada project A.TEP. 280.UGR18, I+D+I Junta de Andalucia 2020 project P20-00200, and Fapergs/Capes do Brasil grant 19/25510000928-3. Funding for open-access charge: Universidad de Granada/CBUAObjective: To perform a comprehensive review of the use of artificial intelligence
(AI) and machine learning (ML) in dentistry, providing the community with a broad
insight on the different advances that these technologies and tools have produced,
paying special attention to the area of esthetic dentistry and color research.
Materials and methods: The comprehensive review was conducted in MEDLINE/
PubMed, Web of Science, and Scopus databases, for papers published in English language
in the last 20 years.
Results: Out of 3871 eligible papers, 120 were included for final appraisal. Study
methodologies included deep learning (DL; n = 76), fuzzy logic (FL; n = 12), and other
ML techniques (n = 32), which were mainly applied to disease identification, image
segmentation, image correction, and biomimetic color analysis and modeling.
Conclusions: The insight provided by the present work has reported outstanding
results in the design of high-performance decision support systems for the aforementioned
areas. The future of digital dentistry goes through the design of integrated
approaches providing personalized treatments to patients. In addition, esthetic dentistry
can benefit from those advances by developing models allowing a complete
characterization of tooth color, enhancing the accuracy of dental restorations.
Clinical significance: The use of AI and ML has an increasing impact on the dental
profession and is complementing the development of digital technologies and tools,
with a wide application in treatment planning and esthetic dentistry procedures.Spanish Ministry of Sciences, Innovation and Universities RTI2018-101674-B-I00
PGC2018-101904-A-100University of Granada project A.TEP. 280.UGR18Junta de Andalucia P20-00200Fapergs/Capes do Brasil grant 19/25510000928-3Universidad de Granada/CBU
Validation of a Hyperspectral Imaging System for Color Measurement of In-Vivo Dental Structures
A full comprehension of colorimetric relationships within and between teeth is key for aesthetic success of a dental restoration. In this sense, hyperspectral imaging can provide point-wise reliable measurements of the tooth surface, which can serve for this purpose. The aim of this study was to use a hyperspectral imaging system for the colorimetric characterization of 4 in-vivo maxillary anterior teeth and to cross-check the results with similar studies carried out with other measuring systems in order to validate the proposed capturing protocol. Hyperspectral reflectance images (Specim IQ), of the upper central (UCI) and lateral incisors (ULI), were captured on 30 participants. CIE-L*a*b* values were calculated for the incisal (I), middle (M) and cervical (C) third of each target tooth. Delta E-ab* and Delta E-00 total color differences were computed between different tooth areas and adjacent teeth, and evaluated according to the perceptibility (PT) and acceptability (AT) thresholds for dentistry. Non-perceptible color differences were found between UCIs and ULIs. Mean color differences between UCI and ULI exceeded AT (Delta E-ab* = 7.39-7.42; Delta E-00 = 5.71-5.74) in all cases. Large chromatic variations between I, M and C areas of the same tooth were registered (Delta E-ab* = 5.01-6.07 and Delta E-00 = 4.07-5.03; Delta E-ab* = 5.80-8.16 and Delta E-00 = 4.37-5.15; and Delta E-ab* = 5.42-5.92 and Delta E-00 = 3.87-4.16 between C and M, C and I and M and I, respectively). The use of a hyperspectral camera has proven to be a reliable and effective method for color evaluation of in-vivo natural teeth.MCIN/AEI/ERDF "Una manera de hacer Europa" PGC2018-101904-A-I00
PID2021128317OB-I00Junta de Andalucia RDI P20-00200OTRI 474
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