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

Code for "The Box Size Confidence Bias Harms Your Object Detector"

LoL Runes Recommender With Python

A clear, concise, simple yet powerful and efficient API for deep learning.

Automatic Differentiation Multipole Moment Molecular Forcefield

Learning Saliency Propagation for Semi-supervised Instance Segmentation

Online-compatible Unsupervised Non-resonant Anomaly Detection Repository

Unofficial implementation of Point-Unet: A Context-Aware Point-Based Neural Network for Volumetric Segmentation

ProjectOxford-ClientSDK - This repo has moved :house: Visit our website for the latest SDKs & Samples

Heat transfer problemas solved using python

A library for implementing Decentralized Graph Neural Network algorithms.

Locally Constrained Self-Attentive Sequential Recommendation

Hierarchical Memory Matching Network for Video Object Segmentation (ICCV 2021)

Logistic Bandit experiments. Official code for the paper "Jointly Efficient and Optimal Algorithms for Logistic Bandits".

From the basics to slightly more interesting applications of Tensorflow

Flexible Option Learning - NeurIPS 2021

Official implementation of the NRNS paper: No RL, No Simulation: Learning to Navigate without Navigating

Mapping Conditional Distributions for Domain Adaptation Under Generalized Target Shift

Creating a custom CNN hypertunned architeture for the Fashion MNIST dataset with Python, Keras and Tensorflow.

Pytorch implementation of AREL

Multi-Task Temporal Shift Attention Networks for On-Device Contactless Vitals Measurement (NeurIPS 2020)