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@inproceedings{1644098,
author = {Akbas, Cem Emre and Kozubek, Michal},
address = {Iowa},
booktitle = {IEEE 17th International Symposium on Biomedical Imaging},
doi = {http://dx.doi.org/10.1109/ISBI45749.2020.9098351},
keywords = {Biomedical Image Segmentation; Convolutional Neural Networks; Deep Learning; Feature Learning; Max Pooling},
howpublished = {elektronická verze "online"},
language = {eng},
location = {Iowa},
isbn = {978-1-5386-9330-8},
pages = {446-450},
publisher = {IEEE},
title = {Condensed U-Net (CU-Net): An Improved U-Net Architecture for Cell Segmentation Powered by 4x4 Max-Pooling Layers},
url = {https://ieeexplore.ieee.org/abstract/document/9098351},
year = {2020}
}
TY - JOUR
ID - 1644098
AU - Akbas, Cem Emre - Kozubek, Michal
PY - 2020
TI - Condensed U-Net (CU-Net): An Improved U-Net Architecture for Cell Segmentation Powered by 4x4 Max-Pooling Layers
PB - IEEE
CY - Iowa
SN - 9781538693308
KW - Biomedical Image Segmentation
KW - Convolutional Neural Networks
KW - Deep Learning
KW - Feature Learning
KW - Max Pooling
UR - https://ieeexplore.ieee.org/abstract/document/9098351
L2 - https://ieeexplore.ieee.org/abstract/document/9098351
N2 - Recently, the U-Net has been the dominant approach in the cell segmentation task in biomedical images due to its success in a wide range of image recognition tasks. However, recent studies did not focus enough on updating the architecture of the U-Net and designing specialized loss functions for bioimage segmentation. We show that the U-Net architecture can achieve more successful results with efficient architectural improvements. We propose a condensed encoder-decoder scheme that employs the 4x4 max-pooling operation and triple convolutional layers. The proposed network architecture is trained using a novel combined loss function specifically designed for bioimage segmentation. On the benchmark datasets from the Cell Tracking Challenge, the experimental results show that the proposed cell segmentation system outperforms the U-Net.
ER -
AKBAS, Cem Emre and Michal KOZUBEK. Condensed U-Net (CU-Net): An Improved U-Net Architecture for Cell Segmentation Powered by 4x4 Max-Pooling Layers. Online. In \textit{IEEE 17th International Symposium on Biomedical Imaging}. Iowa: IEEE, 2020. p.~446-450. ISBN~978-1-5386-9330-8. Available from: https://dx.doi.org/10.1109/ISBI45749.2020.9098351. [citováno 2024-04-24]
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