An efficient memory reserving-and-fading strategy for vector quantization based 3D brain segmentation and tumor extraction using an unsupervised deep learning network.
- DOI
- 10.1007/s11571-023-09965-9
- Published
- 2023-04-26
- Container
- Cogn Neurodyn
- Publisher
- Not recorded
- Open access
- yes
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Cite this work
BibTeX
@article{allodium:10.1007/s11571-023-09965-9,
title = {An efficient memory reserving-and-fading strategy for vector quantization based 3D brain segmentation and tumor extraction using an unsupervised deep learning network.},
author = {De A and Wang X and Zhang Q and Wu J and Cong F.},
year = {2023},
journal = {Cogn Neurodyn},
doi = {10.1007/s11571-023-09965-9},
url = {https://doi.org/10.1007/s11571-023-09965-9}
}RIS
TY - JOUR TI - An efficient memory reserving-and-fading strategy for vector quantization based 3D brain segmentation and tumor extraction using an unsupervised deep learning network. AU - De A AU - Wang X AU - Zhang Q AU - Wu J AU - Cong F. PY - 2023 JO - Cogn Neurodyn DO - 10.1007/s11571-023-09965-9 UR - https://doi.org/10.1007/s11571-023-09965-9 ER -
APA
A, D., X, W., Q, Z., J, W., & F., C. (2023). An efficient memory reserving-and-fading strategy for vector quantization based 3D brain segmentation and tumor extraction using an unsupervised deep learning network.. Cogn Neurodyn. https://doi.org/10.1007/s11571-023-09965-9
Source records
- europe-pmc · retrieved 2026-09-25T05:08:45.652Z