Multiobjective optimization of deep neural networks with combinations of Lp-norm cost functions for 3D medical image super-resolution-Reference-Cited by-同舟云学术

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Multiobjective optimization of deep neural networks with combinations of Lp-norm cost functions for 3D medical image super-resolution

Published:2020-05-20 Issue:3 Volume:27 Page:233-251
ISSN:1069-2509
Container-title:Integrated Computer-Aided Engineering
language:
Short-container-title:ICA

Author:

Thurnhofer-Hemsi Karl¹,López-Rubio Ezequiel¹,Roé-Vellvé Núria²,Molina-Cabello Miguel A.¹

Affiliation:

1. Department of Computer Languages and Computer Science, University of Málaga, Institute of Biomedical Research of Málaga (IBIMA), Málaga, Spain

2. Biomedical Research Networking Center in Bioengineering, Biomaterials and Nanomedicine (CIBER-BBN), Barcelona, Spain

Publisher

IOS Press

Subject

Artificial Intelligence,Computational Theory and Mathematics,Computer Science Applications,Theoretical Computer Science,Software

Reference66 articles.

1. Multiscale brain MRI super-resolution using deep 3D convolutional networks;Pham;Computerized Medical Imaging and Graphics.,2019

2. Segmentation and clustering in brain MRI imaging;Mirzaei;Reviews in the Neurosciences.,2018

3. Chong JJR. Deep-Learning Super-Resolution MRI: Getting Something From Nothing. Journal of Magnetic Resonance Imaging. 2019.

4. Prince JL, Carass A, Zhao C, Dewey BE, Roy S, Pham DL. Chapter 1 - Image synthesis and superresolution in medical imaging. In: Zhou SK, Rueckert D, Fichtinger G, editors. Handbook of Medical Image Computing and Computer Assisted Intervention. Academic Press; 2020; pp. 1-24.

5. Zhao X, Zhang Y, Zhang T, Zou X. Channel Splitting Network for Single MR Image Super-Resolution. IEEE Transactions on Image Processing. 2019 Nov; 28(11): 5649-5662.

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