AID-U-Net: An Innovative Deep Convolutional Architecture for Semantic Segmentation of Biomedical Images
- DOI
- 10.3390/diagnostics12122952
- Published
- 2022-11-25
- Container
- Diagnostics
- Publisher
- MDPI AG
- Open access
- unknown
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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 = {Ashkan Tashk and Jürgen Herp and Thomas Bjørsum-Meyer and Anastasios Koulaouzidis and Esmaeil S. Nadimi},
year = {2022},
journal = {Diagnostics},
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 - Ashkan Tashk AU - Jürgen Herp AU - Thomas Bjørsum-Meyer AU - Anastasios Koulaouzidis AU - Esmaeil S. Nadimi PY - 2022 JO - Diagnostics DO - 10.3390/diagnostics12122952 UR - https://doi.org/10.3390/diagnostics12122952 ER -
APA
Tashk, A., Herp, J., Bjørsum-Meyer, T., Koulaouzidis, A., & Nadimi, E. S. (2022). AID-U-Net: An Innovative Deep Convolutional Architecture for Semantic Segmentation of Biomedical Images. Diagnostics. https://doi.org/10.3390/diagnostics12122952
Source records
- crossref · retrieved 2026-09-25T05:15:17.110Z