J
José Santamaría
Researcher at University of Jaén
Publications - 56
Citations - 2537
José Santamaría is an academic researcher from University of Jaén. The author has contributed to research in topics: Image registration & Evolutionary computation. The author has an hindex of 20, co-authored 51 publications receiving 1200 citations. Previous affiliations of José Santamaría include University of Granada & University of Cádiz.
Papers
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Journal ArticleDOI
Review of deep learning: concepts, CNN architectures, challenges, applications, future directions
Laith Alzubaidi,Jinglan Zhang,Amjad J. Humaidi,Ayad Q. Al-Dujaili,Ye Duan,Omran Al-Shamma,José Santamaría,Mohammed A. Fadhel,Muthana Al-Amidie,Laith Farhan +9 more
TL;DR: In this paper, a comprehensive survey of the most important aspects of DL and including those enhancements recently added to the field is provided, and the challenges and suggested solutions to help researchers understand the existing research gaps.
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Towards a Better Understanding of Transfer Learning for Medical Imaging: A Case Study
Laith Alzubaidi,Mohammed A. Fadhel,Omran Al-Shamma,Jinglan Zhang,José Santamaría,Ye Duan,Sameer Razzaq Oleiwi +6 more
TL;DR: A deep convolutional neural network (DCNN) model that integrates three ideas including traditional and parallel Convolutional layers and residual connections along with global average pooling is designed that can significantly improve the performance considering a reduced number of images in the same domain of the target dataset.
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Novel Transfer Learning Approach for Medical Imaging with Limited Labeled Data.
Laith Alzubaidi,Muthana Al-Amidie,Ahmed Al-Asadi,Amjad J. Humaidi,Omran Al-Shamma,Mohammed A. Fadhel,Jinglan Zhang,José Santamaría,Ye Duan +8 more
TL;DR: A novel transfer learning approach to overcome the previous drawbacks by means of training the deep learning model on large unlabeled medical image datasets and by next transferring the knowledge to train the deepLearning model on the small amount of labeled medical images is proposed.
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A comparative study of state-of-the-art evolutionary image registration methods for 3D modeling
TL;DR: This contribution aims to review the state-of-the-art image registration methods that lay their foundations on evolutionary computation and aims to analyze the performance of some of the latter approaches when tackle a challenging real-world application in forensic anthropology, the 3D modeling of forensic objects.
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Medical Image Registration Using Evolutionary Computation: An Experimental Survey
TL;DR: An experimental survey of the most recognized feature-based medical IR methods considering evolutionary algorithms and other metaheuristics, and benchmarked on two real-world medical scenarios considering two data sets of three-dimensional images with different modalities.