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Characterizing ResNet Filters to Identify Positive and Negative Findings in Breast MRI Sequences
Conference ObjectAbstract: Training of deep learning models requires large and properly labeled datasets, which make unfeasiblePalabras claves:Breast Cancer, Deep feature selection, multiple kernel learning, ResNet, Transfer learningAutores:A. E. Castro-Ospina, Díaz G.M., Hernández M.L., Marín-Castrillón D.M., Osorno-Castillo K.Fuentes:scopusAnalysis of motor imaginary BCI within multi-environment scenarios using a mixture of classifiers
Conference ObjectAbstract: Brain-computer interface (BCI) is a system that provides communication between human beings and machPalabras claves:Brain-Computer Interface, ENVIRONMENTS, Mixture of classifiers, Signal processingAutores:A. E. Castro-Ospina, Diego Hernán Peluffo-Ordóñez, Marín-Castrillón D.M., Miguel A. Becerra, Moreno-Revelo M.Y., Ortega-Adarme M.Fuentes:scopusFusion of 3D Radiomic Features from Multiparametric Magnetic Resonance Images for Breast Cancer Risk Classification
Conference ObjectAbstract: Radiomics imaging technology refers to the computation of a large number of quantitative features toPalabras claves:Breast Cancer, Fusion strategies, MRI sequences, RadiomicsAutores:A. E. Castro-Ospina, Díaz G.M., Hernández M.L., Marín-Castrillón D.M., Rincón J.S.Fuentes:scopusExploration of characterization and classification techniques for movement identification from EMG signals: Preliminary results
Conference ObjectAbstract: Today, human-computer interfaces are increasingly more often used and become necessary for human daiPalabras claves:classification, EMG signals, Movements selection, WaveletAutores:A. E. Castro-Ospina, Diego Hernán Peluffo-Ordóñez, Javier E. Revelo-Fuelagán, Laura Daniela Lasso-Arciniegas, Miguel A. Becerra, Salazar-Castro J.A., Viveros-Melo A.Fuentes:scopusFeature group selection using mkl penalized with ℓ<inf>1</inf> -norm and svm as base learner
Conference ObjectAbstract: Objective feature selection is an important component in the machine learning framework, which has aPalabras claves:feature selection, Group LASSO, Multimodality, multiple kernel learning, sparsityAutores:A. E. Castro-Ospina, Areiza-Laverde H.J., Díaz G.M.Fuentes:scopusOdor pleasantness classification from electroencephalographic signals and emotional states
Conference ObjectAbstract: Odor identification refers to the capability of the olfactory sense for discerning odors. The interePalabras claves:Electroencephalographic signal, Emotion, Odor pleasantness, Sensorial stimuli, Signal processingAutores:A. E. Castro-Ospina, Diego Hernán Peluffo-Ordóñez, Londoño-Delgado E., Marín-Castrillón D.M., Miguel A. Becerra, Pelaez-Becerra S.M., Serna-Guarin L.Fuentes:scopusMachine learning methods for classifying mammographic regions using the wavelet transform and radiomic texture features
Conference ObjectAbstract: Automatic detection and classification of lesions in mammography remains one of the most important aPalabras claves:Breast Cancer, Machine learning methods, Radiomics, ROI classificationAutores:A. E. Castro-Ospina, Díaz G.M., Fabián R. Narváez, Rincón J.S.Fuentes:googlescopusTwo novel clustering performance measures based on coherence and relative assignments of clusters
Conference ObjectAbstract: This work proposes two novel alternatives for dealing with the highly important issue of the clusterPalabras claves:Cluster coherence, Clustering, Graph-partitioning, probabilities, Relative frequenciesAutores:A. E. Castro-Ospina, Areiza-Laverde H.J., Diego Hernán Peluffo-Ordóñez, Miguel A. Becerra, Paul D. Rosero-Montalvo, Rodríguez-Sotelo J.L.Fuentes:scopusVoice pathology detection using artificial neural networks and support vector machines powered by a multicriteria optimization algorithm
Conference ObjectAbstract: Computer-aided diagnosis (CAD) systems have allowed to enhance the performance of conventional, mediPalabras claves:classification, Computer-aided diagnosis, Optimization, Voice pathologyAutores:A. E. Castro-Ospina, Areiza-Laverde H.J., Diego Hernán Peluffo-OrdóñezFuentes:scopus