REFERENCES & ACKNOWLEDGMENTS

Citing GAN-MAT

If you use GAN-MAT for your project, please cite the following:

GAN-MAT: Generative Adversarial Network-based Microstructural Profile Covariance Analysis Toolbox.

References

  • GAN

    Isola, Phillip, et al. “Image-to-image translation with conditional adversarial networks.” Proceedings of the IEEE conference on computer vision and pattern recognition. 2017. https://doi.org/10.48550/arXiv.1611.07004

  • FSL

    Jenkinson, M., Beckmann, C. F., Behrens, T. E., Woolrich, M. W., & Smith, S. M. (2012). FSL. NeuroImage, 62(2), 782–790. https://www.sciencedirect.com/science/article/pii/S1053811911010603

  • FreeSurfer

    Fischl B. FreeSurfer. Neuroimage. 2012 Aug 15;62(2):774-81. Epub 2012 Jan 10. PMID: 22248573; PMCID: PMC3685476. https://pubmed.ncbi.nlm.nih.gov/22248573/

  • Workbench

    Marcus DS, Harwell J, Olsen T, Hodge M, Glasser MF, Prior F, Jenkinson M, Laumann T, Curtiss SW, and Van Essen DC. (2011). Informatics and data mining: Tools and strategies for the Human Connectome Project. Frontiers in Neuroinformatics 5:4. https://doi.org/10.3389/fninf.2011.00004

  • GNU Parallel

    Tange, Ole. (2018). GNU Parallel 2018. In GNU Parallel 2018 (p. 112). Ole Tange. https://doi.org/10.5281/zenodo.1146014

  • brainspace

    Vos de Wael, R., Benkarim, O., Paquola, C. et al. BrainSpace: a toolbox for the analysis of macroscale gradients in neuroimaging and connectomics datasets. Commun Biol 3, 103 (2020). https://doi.org/10.1038/s42003-020-0794-7

  • numpy

    Walt, S.V., Colbert, S.C., & Varoquaux, G. (2011). The NumPy Array: A Structure for Efficient Numerical Computation. Computing in Science & Engineering, 13, 22-30. https://doi.org/10.48550/arXiv.1102.1523

  • scipy

    Virtanen, P., Gommers, R., Oliphant, T. E., Haberland, M., Reddy, T., Cournapeau, D., … & Van Mulbregt, P. (2020). SciPy 1.0: fundamental algorithms for scientific computing in Python. Nat Methods 17, 261–272. https://doi.org/10.1038/s41592-019-0686-2

  • nibabel

    Matthew Brett (MB), Chris Markiewicz (CM), Michael Hanke (MH), Marc-Alexandre Côté (MC), Ben Cipollini (BC), Paul McCarthy (PM), Chris Cheng (CC), Yaroslav Halchenko (YOH), Satra Ghosh (SG), Eric Larson (EL), Demian Wassermann, Stephan Gerhard and Ross Markello (RM). (2020). NiBabel 3.1.1. Jun 30. https://nipy.org/nibabel/

  • torch

    Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., … Chintala, S. (2019). PyTorch: An Imperative Style, High-Performance Deep Learning Library. In Advances in Neural Information Processing Systems 32 (pp. 8024–8035). Curran Associates, Inc. Retrieved from http://papers.neurips.cc/paper/9015-pytorch-an-imperative-style-high-performance-deep-learning-library.pdf

  • torchvision

    Sébastien Marcel and Yann Rodriguez. 2010. Torchvision the machine-vision package of torch. In Proceedings of the 18th ACM international conference on Multimedia (MM ‘10). Association for Computing Machinery, New York, NY, USA, 1485–1488. https://doi.org/10.1145/1873951.1874254

  • scikit-image

    van der Walt S, Schönberger JL, Nunez-Iglesias J, Boulogne F, Warner JD, Yager N, Gouillart E, Yu T; scikit-image contributors. scikit-image: image processing in Python. PeerJ. 2014 Jun 19;2:e453. doi: 10.7717/peerj.453. PMID: 25024921; PMCID: PMC4081273. https://doi.org/10.7717/peerj.453

  • tqdm

    da Costa-Luis, (2019). tqdm: A Fast, Extensible Progress Meter for Python and CLI. Journal of Open Source Software, 4(37), 1277, https://doi.org/10.21105/joss.01277

Acknowledgments

The authors would like to express their gratitude to the open science initiatives that made this work possible:

  • National Research Foundation of Korea

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  • nstitute for Information and Communications Technology Planning and Evaluation (IITP) funded by the Korea Government (MSIT)

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  • Institute for Basic Science

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