The DOI matches its Crossref record; 19 of the 19 references it cites were found in a registry or an index.
MultiResUNet : Rethinking the U-Net architecture for multimodal biomedical image segmentation
Open accessAbstract
In recent years Deep Learning has brought about a breakthrough in Medical Image Segmentation. In this regard, U-Net has been the most popular architecture in the medical imaging community. Despite outstanding overall performance in segmenting multimodal medical images, through extensive experimentations on some challenging datasets, we demonstrate that the classical U-Net architecture seems to be lacking in certain aspects. Therefore, we propose some modifications to improve upon the already state-of-the-art U-Net model. Following these modifications, we develop a novel architecture, MultiResUNet, as the potential successor to the U-Net architecture. We have tested and compared MultiResUNet with the classical U-Net on a vast repertoire of multimodal medical images. Although only slight improvements in the cases of ideal images are noticed, remarkable gains in performance have been attained for the challenging ones. We have evaluated our model on five different datasets, each with their own unique challenges, and have obtained a relative improvement in performance of 10.15%, 5.07%, 2.63%, 1.41%, and 0.62% respectively. We have also discussed and highlighted some qualitatively superior aspects of MultiResUNet over classical U-Net that are not really reflected in the quantitative measures.
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Deep Residual Learning for Image Recognition
He, K., Zhang, X., Ren, S., & Sun, J. (2016) · 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2016.90 · Cited by 235,474
ImageNet classification with deep convolutional neural networks
Krizhevsky, A., Sutskever, I., & Hinton, G. E. (2017) · Communications of the ACM · doi:10.1145/3065386 · Cited by 112,155
U-Net: Convolutional Networks for Biomedical Image Segmentation
Ronneberger, O., Fischer, P., & Brox, T. (2015) · Lecture notes in computer science · doi:10.1007/978-3-319-24574-4_28 · Cited by 99,404
Adam: A Method for Stochastic Optimization
Kingma, D. P., & Ba, J. (2014) · arXiv · doi:10.48550/arxiv.1412.6980 · Cited by 98,946
LeCun, Y., Bengio, Y., & Hinton, G. (2015) · Nature · doi:10.1038/nature14539 · Cited by 84,126
Gradient-based learning applied to document recognition
Lecun, Y., Bottou, L., Bengio, Y., & Haffner, P. (1998) · Proceedings of the IEEE · doi:10.1109/5.726791 · Cited by 58,805
Rethinking the Inception Architecture for Computer Vision
Szegedy, C., Vanhoucke, V., Ioffe, S., Shlens, J., & Wojna, Z. (2016) · 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2016.308 · Cited by 32,138
Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift
Ioffe, S., & Szegedy, C. (2015) · arXiv · doi:10.48550/arxiv.1502.03167 · Cited by 27,818
Chen, L.-C., Papandreou, G., Kokkinos, I., Murphy, K., & Yuille, A. L. (2016) · arXiv · doi:10.48550/arxiv.1606.00915 · Cited by 22,392
SegNet: A Deep Convolutional Encoder-Decoder Architecture for Image Segmentation
Badrinarayanan, V., Kendall, A., & Cipolla, R. (2017) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2016.2644615 · Cited by 19,631
Cited by
Loss odyssey in medical image segmentation
Ma, J., Chen, J., Ng, M., Huang, R., Li, Y., Li, C., Yang, X., & Martel, A. L. (2021) · Medical Image Analysis · doi:10.1016/j.media.2021.102035 · Cited by 662
Focus U-Net: A novel dual attention-gated CNN for polyp segmentation during colonoscopy
Yeung, M., Sala, E., Schönlieb, C.-B., & Rundo, L. (2021) · Computers in Biology and Medicine · doi:10.1016/j.compbiomed.2021.104815 · Cited by 186
PPG2ABP: Translating photoplethysmogram (PPG) signals to arterial blood pressure (ABP) waveforms
Ibtehaz, N., Mahmud, S., Chowdhury, M. E. H., Khandakar, A., Salman Khan, M., Ayari, M. A., Tahir, A. M., & Rahman, M. S. (2022) · Bioengineering · doi:10.3390/bioengineering9110692 · Cited by 133
Self-citationPei, X., Zuo, K., Li, Y., & Pang, Z. (2023) · International Journal of Computational Intelligence Systems · doi:10.1007/s44196-023-00225-6 · Cited by 77
A survey on deep learning in medical ultrasound imaging
Song, K., Feng, J., & Chen, D. (2024) · Frontiers in Physics · doi:10.3389/fphy.2024.1398393 · Cited by 59
Metadata
- Authors
- Ibtehaz, N., & Rahman, M. S.
- Venue
- Neural Networks
- Publisher
- Elsevier BV
- Published
- 2020-01
- Volume
- 121
- Pages
- 74-87
- ISSN
- 0893-6080 (ISSN-L)
- OpenAlex
- W2928133111
- Cited by
- 2,311
- References
- 19
- FWCI
- 86.14
- Type
- journal article
- Language
- en
- Open access
- green
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- Ibtehaz, N., & Rahman, M. S. (2020). MultiResUNet : Rethinking the U-Net architecture for multimodal biomedical image segmentation. Neural Networks. https://doi.org/10.1016/j.neunet.2019.08.025
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