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

PyTorch implementations of algorithms for density estimation

Expressive Power of Invariant and Equivaraint Graph Neural Networks (ICLR 2021)

State-of-the-art data augmentation search algorithms in PyTorch

MediaPipeのPythonパッケージのサンプルです。2020/12/11時点でPython実装のある4機能(Hands、Pose、Face Mesh、Holistic)について用意しています。

最新版本yolov5+deepsort目标检测和追踪，支持5.0版本可训练自己数据集

RL algorithm PPO and IRL algorithm AIRL written with Tensorflow.

pytorch implementation of openpose including Hand and Body Pose Estimation.

Pytorch implementation of paper "Efficient Nearest Neighbor Language Models" (EMNLP 2021)

A modular PyTorch library for optical flow estimation using neural networks

A Deep Convolutional Encoder-Decoder Architecture for Image Segmentation

Tools for the Cleveland State Human Motion and Control Lab

Metrics to evaluate quality and efficacy of synthetic datasets.

[LREC] MMChat: Multi-Modal Chat Dataset on Social Media

A framework that allows people to write their own Rocket League bots.

A curated (most recent) list of resources for Learning with Noisy Labels

A Collection of Papers and Codes for ICCV2021 Low Level Vision and Image Generation

Learned model to estimate number of distinct values (NDV) of a population using a small sample.

Experiments for Operating Systems Lab (ETCS-352)

Official implementation of Densely connected normalizing flows

QHack—the quantum machine learning hackathon