Official PyTorch implementation of UACANet: Uncertainty Aware Context Attention for Polyp Segmentation

Last update: Dec 14, 2022

Overview

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 data to 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.yaml to 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!

Official PyTorch implementation of UACANet: Uncertainty Aware Context Attention for Polyp Segmentation

Related tags

Overview

UACANet: Uncertainty Aware Context Attention for Polyp Segmentation

Abstract

1. Create environment

2. Prepare datasets

3. Train & Evaluate

4. Experimental Results

5. Citation

6. Acknowledgement

Owner

Taehun Kim

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Official PyTorch implementation of UACANet: Uncertainty Aware Context Attention for Polyp Segmentation

Related tags

Overview

UACANet: Uncertainty Aware Context Attention for Polyp Segmentation

Abstract

1. Create environment

2. Prepare datasets

3. Train & Evaluate

4. Experimental Results

5. Citation

6. Acknowledgement

Owner

Taehun Kim

Deep Learning for Time Series Forecasting.

A simple command line tool for text to image generation, using OpenAI's CLIP and a BigGAN.

A large-scale video dataset for the training and evaluation of 3D human pose estimation models

Ludwig is a toolbox that allows to train and evaluate deep learning models without the need to write code.

OpenGAN: Open-Set Recognition via Open Data Generation

DRIFT is a tool for Diachronic Analysis of Scientific Literature.

CM-NAS: Cross-Modality Neural Architecture Search for Visible-Infrared Person Re-Identification (ICCV2021)

Neural Cellular Automata + CLIP

All the code and files related to the MI-Lab of UE19CS305 course in sem 5

i3DMM: Deep Implicit 3D Morphable Model of Human Heads

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A user-friendly research and development tool built to standardize RL competency assessment for custom agents and environments.

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PyTorch implementation of Algorithm 1 of "On the Anatomy of MCMC-Based Maximum Likelihood Learning of Energy-Based Models"

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