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

Implementation of "Generalizable Neural Performer: Learning Robust Radiance Fields for Human Novel View Synthesis"

A PyTorch implementation of Learning to learn by gradient descent by gradient descent

This is code to fit per-pixel environment map with spherical Gaussian lobes, using LBFGS optimization

PyTorch implementations of Top-N recommendation, collaborative filtering recommenders.

Official repository for the ISBI 2021 paper Transformer Assisted Convolutional Neural Network for Cell Instance Segmentation

TorchGRL is the source code for our paper Graph Convolution-Based Deep Reinforcement Learning for Multi-Agent Decision-Making in Mixed Traffic Environments for IV 2022.

3D-Transformer: Molecular Representation with Transformer in 3D Space

An AutoML Library made with Optuna and PyTorch Lightning

Everything about being a TA for ITP/AP course!

Modifications of the official PyTorch implementation of StyleGAN3. Let's easily generate images and videos with StyleGAN2/2-ADA/3!

Source code for "Taming Visually Guided Sound Generation" (Oral at the BMVC 2021)

[IEEE TPAMI21] MobileSal: Extremely Efficient RGB-D Salient Object Detection [PyTorch & Jittor]

Research Artifact of USENIX Security 2022 Paper: Automated Side Channel Analysis of Media Software with Manifold Learning

PyTorch implementation of CloudWalk's recent work DenseBody

Code and real data for the paper "Counterfactual Temporal Point Processes", available at arXiv.

[PAMI 2020] Show, Match and Segment: Joint Weakly Supervised Learning of Semantic Matching and Object Co-segmentation

WaveFake: A Data Set to Facilitate Audio DeepFake Detection

Multimodal Temporal Context Network (MTCN)

Teaches a student network from the knowledge obtained via training of a larger teacher network

SciPy fixes and extensions