ResUNet++ is an advanced and more accurate version of the standard U-Net and ResNet architectures, tailored specifically for medical image segmentation tasks. It was proposed to address certain limitations of U-Net and further enhance the accuracy and efficiency of medical image segmentation.
Modified Stem Block: Unlike U-Net, ResUNet++ starts with a modified stem block that reduces the spatial dimensions of the input image.
Residual Blocks: Residual connections are employed in the encoder path. These connections bypass one or more layers and help in addressing the vanishing gradient problem, leading to deeper networks.
Squeeze and excitation network: The Squeeze and exictation blocks provide dynamic channel-wise recalibration, enhancing the model's ability to focus on significant features.
Attention Gates: The architecture introduces attention gates to the skip connections, allowing the model to focus on specific features more prominently.
Redesigned Decoder Path: The decoder path of ResUNet++ employs up-convolution followed by a series of convolutions and is equipped with long-range skip connections to gather multi-scale contextual information.
ASPP (Atrous Spatial Pyramid Pooling): To capture multi-scale contextual information, ASPP is used in the last layer before the final output.
- Improved accuracy for medical image segmentation.
- Efficient learning of hierarchical features.
- Ability to capture long-range spatial dependencies.
- Medical Image Segmentation
- General Image Segmentation
- Anomaly Detection in Medical Images
- Comparative Studies
Qualitative results comparison on the Kvasir-SEG dataset.From the left: image (1), (2) Ground truth, (3) U-Net, (4)
ResUNet, (5) ResUNet-mod, and (6) ResUNet++.
Please cite our paper if you find the work useful:
@INPROCEEDINGS{8959021,
author={D. {Jha} and P. H. {Smedsrud} and M. A. {Riegler} and D. {Johansen} and T. D. {Lange} and P. {Halvorsen} and H. {D. Johansen}},
booktitle={Proceedings of the IEEE International Symposium on Multimedia (ISM)},
title={ResUNet++: An Advanced Architecture for Medical Image Segmentation},
year={2019},
pages={225-230}}
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