AID-U-Net: An Innovative Deep Convolutional Architecture for Semantic Segmentation of Biomedical Images.
- DOI
- 10.3390/diagnostics12122952
- Published
- 2022 Nov 25
- Container
- Diagnostics (Basel, Switzerland)
- Publisher
- Not recorded
- Open access
- yes
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Cite this work
BibTeX
@article{allodium:10.3390/diagnostics12122952,
title = {AID-U-Net: An Innovative Deep Convolutional Architecture for Semantic Segmentation of Biomedical Images.},
author = {Tashk A and Herp J and Bjørsum-Meyer T and Koulaouzidis A and Nadimi ES},
year = {2022},
journal = {Diagnostics (Basel, Switzerland)},
doi = {10.3390/diagnostics12122952},
url = {https://doi.org/10.3390/diagnostics12122952}
}RIS
TY - JOUR TI - AID-U-Net: An Innovative Deep Convolutional Architecture for Semantic Segmentation of Biomedical Images. AU - Tashk A AU - Herp J AU - Bjørsum-Meyer T AU - Koulaouzidis A AU - Nadimi ES PY - 2022 JO - Diagnostics (Basel, Switzerland) DO - 10.3390/diagnostics12122952 UR - https://doi.org/10.3390/diagnostics12122952 ER -
APA
A, T., J, H., T, B., A, K., & ES, N. (2022). AID-U-Net: An Innovative Deep Convolutional Architecture for Semantic Segmentation of Biomedical Images.. Diagnostics (Basel, Switzerland). https://doi.org/10.3390/diagnostics12122952
Source records
- pubmed · retrieved 2026-11-10T07:15:49.493Z