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UNet++: A Nested U-Net Architecture for Medical Image Segmentation
UNet++ is a new general purpose image segmentation architecture for more accurate image segmentation. UNet++ consists of U-Nets of varying depths whose decoders are densely connected at the same resolution via the redesigned skip pathways, which aim to address two key challenges of the U-Net: 1) unknown depth of the optimal architecture and 2) the unnecessarily restrictive design of skip connections.
Paper
This repository provides the official Keras implementation of UNet++ in the following papers:
UNet++: Redesigning Skip Connections to Exploit Multiscale Features in Image Segmentation <br/> Zongwei Zhou, Md Mahfuzur Rahman Siddiquee, Nima Tajbakhsh, and Jianming Liang <br/> Arizona State University <br/> IEEE Transactions on Medical Imaging (TMI) <br/> paper | code
UNet++: A Nested U-Net Architecture for Medical Image Segmentation <br/> Zongwei Zhou, Md Mahfuzur Rahman Siddiquee, Nima Tajbakhsh, and Jianming Liang <br/> Arizona State University <br/> Deep Learning in Medical Image Analysis (DLMIA) 2018. (Oral) <br/> paper | code | slides | poster | blog
Official implementation
- keras/
- pytorch/
Other implementation
- [PyTorch] (by Pavel Yakubovskiy)
- [PyTorch] (by 4ui_iurz1)
- [PyTorch] (by Hong Jing)
- [PyTorch] (by ZJUGiveLab)
- [PyTorch] (by MontaEllis)
- [Keras] (by Siddhartha)
Citation
If you use UNet++ for your research, please cite our papers:
@article{zhou2019unetplusplus,
title={UNet++: Redesigning Skip Connections to Exploit Multiscale Features in Image Segmentation},
author={Zhou, Zongwei and Siddiquee, Md Mahfuzur Rahman and Tajbakhsh, Nima and Liang, Jianming},
journal={IEEE Transactions on Medical Imaging},
year={2019},
publisher={IEEE}
}
@incollection{zhou2018unetplusplus,
title={Unet++: A Nested U-Net Architecture for Medical Image Segmentation},
author={Zhou, Zongwei and Siddiquee, Md Mahfuzur Rahman and Tajbakhsh, Nima and Liang, Jianming},
booktitle={Deep Learning in Medical Image Analysis and Multimodal Learning for Clinical Decision Support},
pages={3--11},
year={2018},
publisher={Springer}
}
@phdthesis{zhou2021towards,
title={Towards Annotation-Efficient Deep Learning for Computer-Aided Diagnosis},
author={Zhou, Zongwei},
year={2021},
school={Arizona State University}
}
Acknowledgments
This research has been supported partially by NIH under Award Number R01HL128785, by ASU and Mayo Clinic through a Seed Grant and an Innovation Grant. The content is solely the responsibility of the authors and does not necessarily represent the official views of NIH. This is a patent-pending technology.