UACANet: Uncertainty Aware Context Attention for Polyp Segmentation
Official pytorch implementation of UACANet: Uncertainty Aware Context Attention for Polyp Segmentation
To appear in the Proceedings of the 29th ACM International Conference on Multimedia (ACM MM '21)
Abstract
We propose Uncertainty Augmented Context Attention network (UACANet) for polyp segmentation which consider a uncertain area of the saliency map. We construct a modified version of U-Net shape network with additional encoder and decoder and compute a saliency map in each bottom-up stream prediction module and propagate to the next prediction module. In each prediction module, previously predicted saliency map is utilized to compute foreground, background and uncertain area map and we aggregate the feature map with three area maps for each representation. Then we compute the relation between each representation and each pixel in the feature map. We conduct experiments on five popular polyp segmentation benchmarks, Kvasir, CVC-ClinicDB, ETIS, CVC-ColonDB and CVC-300, and achieve state-of-the-art performance. Especially, we achieve 76.6% mean Dice on ETIS dataset which is 13.8% improvement compared to the previous state-of-the-art method.
1. Create environment
- Create conda environment with following command
conda create -n uacanet python=3.7 - Activate environment with following command
conda activate uacanet - Install requirements with following command
pip install -r requirements.txt
2. Prepare datasets
- Download dataset from following URL
- Move folder
datato the repository. - Folder should be ordered as follows,
|-- configs
|-- data
| |-- TestDataset
| | |-- CVC-300
| | | |-- images
| | | `-- masks
| | |-- CVC-ClinicDB
| | | |-- images
| | | `-- masks
| | |-- CVC-ColonDB
| | | |-- images
| | | `-- masks
| | |-- ETIS-LaribPolypDB
| | | |-- images
| | | `-- masks
| | `-- Kvasir
| | |-- images
| | `-- masks
| `-- TrainDataset
| |-- images
| `-- masks
|-- EvaluateResults
|-- lib
| |-- backbones
| |-- losses
| `-- modules
|-- results
|-- run
|-- snapshots
| |-- UACANet-L
| `-- UACANet-S
`-- utils
3. Train & Evaluate
-
You can train with
python run/Train.py --config configs/UACANet-L.yaml -
You can generate prediction for test dataset with
python run/Test.py --config configs/UACANet-L.yaml -
You can evaluate generated prediction with
python run/Eval.py --config configs/UACANet-L.yaml -
You can also use
python Expr.py --config configs/UACANet-L.yamlto train, generate prediction and evaluation in single command -
(optional) Download our best result checkpoint from following URL for UACANet-L and UACANet-S.
4. Experimental Results
- UACANet-S
dataset meanDic meanIoU wFm Sm meanEm mae maxEm maxDic maxIoU meanSen maxSen meanSpe maxSpe
----------------- --------- --------- ----- ----- -------- ----- ------- -------- -------- --------- -------- --------- --------
CVC-300 0.902 0.837 0.886 0.934 0.974 0.006 0.976 0.906 0.840 0.959 1.000 0.992 0.995
CVC-ClinicDB 0.916 0.870 0.917 0.940 0.965 0.008 0.968 0.919 0.873 0.942 1.000 0.991 0.995
Kvasir 0.905 0.852 0.897 0.914 0.948 0.026 0.951 0.908 0.855 0.911 1.000 0.976 0.979
CVC-ColonDB 0.783 0.704 0.772 0.848 0.894 0.034 0.897 0.786 0.706 0.801 1.000 0.958 0.962
ETIS-LaribPolypDB 0.694 0.615 0.650 0.815 0.848 0.023 0.851 0.696 0.618 0.833 1.000 0.887 0.891
- UACANet-L
dataset meanDic meanIoU wFm Sm meanEm mae maxEm maxDic maxIoU meanSen maxSen meanSpe maxSpe
----------------- --------- --------- ----- ----- -------- ----- ------- -------- -------- --------- -------- --------- --------
CVC-300 0.910 0.849 0.901 0.937 0.977 0.005 0.980 0.913 0.853 0.940 1.000 0.993 0.997
CVC-ClinicDB 0.926 0.880 0.928 0.943 0.974 0.006 0.976 0.929 0.883 0.943 1.000 0.992 0.996
Kvasir 0.912 0.859 0.902 0.917 0.955 0.025 0.958 0.915 0.862 0.923 1.000 0.983 0.987
CVC-ColonDB 0.751 0.678 0.746 0.835 0.875 0.039 0.878 0.753 0.680 0.754 1.000 0.953 0.957
ETIS-LaribPolypDB 0.766 0.689 0.740 0.859 0.903 0.012 0.905 0.769 0.691 0.813 1.000 0.932 0.936
- Qualitative Results
5. Citation
@misc{kim2021uacanet,
title={UACANet: Uncertainty Augmented Context Attention for Polyp Semgnetaion},
author={Taehun Kim and Hyemin Lee and Daijin Kim},
year={2021},
eprint={2107.02368},
archivePrefix={arXiv},
primaryClass={cs.CV}
}
- Conference version will be added soon.
6. Acknowledgement
- Basic training strategy, datasets and evaluation methods are brought from PraNet. Especially for the evalutation, we made Python version based on PraNet's MatLab version and verified on various samples. Thanks for the great work!
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