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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: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: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:googlescopus