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

(ICCV 2021) ProHMR - Probabilistic Modeling for Human Mesh Recovery

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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

(ICCV 2021) ProHMR - Probabilistic Modeling for Human Mesh Recovery

Accelerated deep learning R&D

Semantic Segmentation Suite in TensorFlow

We present a regularized self-labeling approach to improve the generalization and robustness properties of fine-tuning.

Action Segmentation Evaluation

A mini-course offered to Undergrad chemistry students

A bare-bones TensorFlow framework for Bayesian deep learning and Gaussian process approximation

An Empirical Investigation of Model-to-Model Distribution Shifts in Trained Convolutional Filters

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Putting NeRF on a Diet: Semantically Consistent Few-Shot View Synthesis

Official PyTorch implementation of DD3D: Is Pseudo-Lidar needed for Monocular 3D Object detection? (ICCV 2021), Dennis Park*, Rares Ambrus*, Vitor Guizilini, Jie Li, and Adrien Gaidon.

Reinforcement learning algorithms in RLlib

EFENet: Reference-based Video Super-Resolution with Enhanced Flow Estimation

This is an open-source toolkit for Heterogeneous Graph Neural Network(OpenHGNN) based on DGL [Deep Graph Library] and PyTorch.

Annotated notes and summaries of the TensorFlow white paper, along with SVG figures and links to documentation

A lightweight library to compare different PyTorch implementations of the same network architecture.

Code for WECHSEL: Effective initialization of subword embeddings for cross-lingual transfer of monolingual language models.

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