J
Juan Manuel Górriz
Researcher at University of Granada
Publications - 403
Citations - 8595
Juan Manuel Górriz is an academic researcher from University of Granada. The author has contributed to research in topics: Support vector machine & Computer science. The author has an hindex of 43, co-authored 360 publications receiving 6429 citations. Previous affiliations of Juan Manuel Górriz include University of Cádiz & University of Cambridge.
Papers
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Journal ArticleDOI
Editorial: Multimodal and Longitudinal Bioimaging Methods for Characterizing the Progressive Course of Dementia
Journal ArticleDOI
Automated Detection and Segmentation of Nonmass-Enhancing Breast Tumors with Dynamic Contrast-Enhanced Magnetic Resonance Imaging
Ignacio A. Illán,Javier Ramírez,Juan Manuel Górriz,Maria Adele Marino,Daly Avendano,Thomas H. Helbich,Pascal A. T. Baltzer,Katja Pinker,Anke Meyer-Baese +8 more
TL;DR: A new approach to address the challenge of NME lesion detection and segmentation is proposed, taking advantage of independent component analysis (ICA) to extract data-driven dynamic lesion characterizations and outperforming previously published approaches.
Book ChapterDOI
New Model for Time-Series Forecasting Using RBFs and Exogenous Data
TL;DR: A new model for time-series forecasting using Radial Basis Functions (RBFs) as a unit of ANNs (Artificial Neural Networks), which allows the inclusion of exogenous information (EI) without additional preprocessing is presented.
Journal ArticleDOI
Diagnosis of brain diseases in fusion of neuroimaging modalities using deep learning: A review
Afshin Shoeibi,Marjane Khodatars,Mahboobeh Jafari,Navid Ghassemi,Parisa Moridian,Roohallah Alizadesani,Sai Ho Ling,Abbas Khosravi,Hamid Alinejad-Rokny,Hak-Keung Lam,Matthew Fuller-Tyszkiewicz,U. Rajendra Acharya,D Anderson,Yudong Zhang,Juan Manuel Górriz +14 more
TL;DR: In this article , the authors present a comprehensive review of brain disease detection from the fusion of neuroimaging modalities using DL models like convolutional neural networks, recurrent neural networks (RNNs), pretrained, generative adversarial networks (GANs), and autoencoders (AEs).
Journal ArticleDOI
Deep Learning in Medical Image Analysis.
TL;DR: In this paper, deep learning has established itself as a powerful tool across a broad spectrum of domains in imaging, such as image classification, image segmentation, and image classification and classification.