https://github.com/nikhilroxtomar/ddanet

DDANet: Dual Decoder Attention Network for automatic Polyp Segmentation [International Conference on Pattern Recognition 2021]
https://github.com/nikhilroxtomar/ddanet

computer-vision deep-learning pytorch semantic-segmentation

Last synced: about 1 year ago
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DDANet: Dual Decoder Attention Network for automatic Polyp Segmentation [International Conference on Pattern Recognition 2021]

• Host: GitHub
• URL: https://github.com/nikhilroxtomar/ddanet
• Owner: nikhilroxtomar
• Created: 2020-11-09T09:45:43.000Z (over 5 years ago)
• Default Branch: main
• Last Pushed: 2023-10-21T20:58:50.000Z (over 2 years ago)
• Last Synced: 2025-03-28T08:11:19.838Z (over 1 year ago)
• Topics: computer-vision, deep-learning, pytorch, semantic-segmentation
• Language: Python
• Homepage: https://arxiv.org/pdf/2012.15245
• Size: 27.4 MB
• Stars: 28
• Watchers: 1
• Forks: 7
• Open Issues: 6
• Metadata Files:
  • Readme: README.md

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README

          
# DDANet: Dual Decoder Attention Network for Automatic Polyp Segmentation

Authors: [Nikhil Kumar Tomar](https://www.linkedin.com/in/nktomar/), [Debesh Jha](https://scholar.google.com/citations?user=mMTyE68AAAAJ&hl=en), Sharib Ali, Håvard D. Johansen, Dag Johansen, Michael A. Riegler and Pål Halvorsen

## Architecture

The proposed DDANet is fully convolutional network consists of a single encoder and dual decoders. The encoder  consists  of  4  encoder  block  whereas  each  decoder  also  consists  of  4 decoder block. The encoder takes the RGB image as input which passes throughthe shared encoder and then it goes through both the decoders. The first decoder gives the segmentation mask and the  second  decoder gives the original input image in the grayscale format.

![DDANet Architecture](figures/EndoTect.png)

## Quantative Results

| Dataset | DSC |  Mean IOU| Recall | Precision | Mean FPS | Mean Time |

| :---: | :---: | :---: | :---: | :---: | :---: | :---: |

| Kvasir Test set | 0.8576 | 0.7800 | 0.8880 | 0.8643 | 70.23445 | 0.014238

| Organiser's Test set | 0.7010 | 0.7874 | 0.7987 | 0.8577 | 69.59296 | 0.014369

## Qualitative Results

![Qualitative Results](figures/figure_name.png)

## Citation

Please cite our paper if you find the work useful: 


@inproceedings{tomar2020ddanet,

  title={DDANet: Dual Decoder Attention Network for Automatic Polyp Segmentation},

  author={Tomar, Nikhil Kumar and Jha, Debesh and Ali, Sharib and Johansen, H{\aa}vard D and Johansen, Dag and Riegler, Michael A and Halvorsen, P{\aa}l},

  booktitle={ICPR International Workshop and Challenges},

  year={2021}

}



## Contact

please contact nikhilroxtomar@gmail.com and debeshjha1@gmail.com for any further questions.