Deep co-supervision and attention fusion strategy for automatic COVID-19 lung infection segmentation on CT images.
- DOI
- 10.1016/j.patcog.2021.108452
- Published
- 2022 Apr
- Container
- Pattern recognition
- Publisher
- Not recorded
- Open access
- yes
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Cite this work
BibTeX
@article{allodium:10.1016/j.patcog.2021.108452,
title = {Deep co-supervision and attention fusion strategy for automatic COVID-19 lung infection segmentation on CT images.},
author = {Hu H and Shen L and Guan Q and Li X and Zhou Q and Ruan S},
year = {2022},
journal = {Pattern recognition},
doi = {10.1016/j.patcog.2021.108452},
url = {https://doi.org/10.1016/j.patcog.2021.108452}
}RIS
TY - JOUR TI - Deep co-supervision and attention fusion strategy for automatic COVID-19 lung infection segmentation on CT images. AU - Hu H AU - Shen L AU - Guan Q AU - Li X AU - Zhou Q AU - Ruan S PY - 2022 JO - Pattern recognition DO - 10.1016/j.patcog.2021.108452 UR - https://doi.org/10.1016/j.patcog.2021.108452 ER -
APA
H, H., L, S., Q, G., X, L., Q, Z., & S, R. (2022). Deep co-supervision and attention fusion strategy for automatic COVID-19 lung infection segmentation on CT images.. Pattern recognition. https://doi.org/10.1016/j.patcog.2021.108452
Source records
- pubmed · retrieved 2026-11-10T13:10:47.624Z