rezazad68 / AttentionDeeplabv3p

Attention Deeplabv3+: Multi-level Context Attention Mechanism for Skin Lesion Segmentation

Attention Deeplabv3+: Multi-level Context Attention Mechanism for Skin Lesion Segmentation

Implementation of Attention Deeplabv3+, an extended version of Deeplabv3+ for skin lesion segmentation by employing the idea of attention mechanism in two stages. In this method, the relationship between the channels of a set of feature maps by assigning a weight for each channel (i.e., channels attention) is captured. In which channel atten-tion allows the network to emphasize more on the informative and meaningful channels by a context gating mechanism. It also exploit the second level attention strategy to integrate different layers of the atrous convolution. It helps thenetwork to focus on the more relevant field of view to the target. If this code helps with your research please consider citing the following papers:

R. Azad, M. Asadi, Mahmood Fathy and Sergio Escalera "Attention Deeplabv3+: Multi-level Context Attention Mechanism for Skin Lesion Segmentation ",ECCV, 2020, download link.

Updates

• Augest 1, 2020: Complete implemenation for SKin Lesion Segmentation task on three different data set has been released.
• Augest 1, 2020: Paper Accepted in the ECCV workshop 2020 (Oral presentation).

Prerequisties and Run

This code has been implemented in python language using Keras libarary with tensorflow backend and tested in ubuntu OS, though should be compatible with related environment. following Environement and Library needed to run the code:

• Python 3
• Keras
• tensorflow backend

Run Demo

For training deep model and evaluating on each data set follow the bellow steps:
1- Download the ISIC 2018 train dataset from this link and extract both training dataset and ground truth folders inside the dataset_isic18.
2- Run Prepare_ISIC2018.py for data preperation and dividing data to train,validation and test sets.
3- Run Train_Skin_Lesion_Segmentation.py for training the model using trainng and validation sets. The model will be train for 100 epochs and it will save the best weights for the valiation set.
4- For performance calculation and producing segmentation result, run Evaluate_Skin.py. It will represent performance measures and will saves related results in output folder.

Notice: For training and evaluating on ISIC 2017 and ph2 follow the bellow steps: :
ISIC 2017- Download the ISIC 2017 train dataset from this link and extract both training dataset and ground truth folders inside the dataset_isic18\7.
then Run Prepare_ISIC2017.py for data preperation and dividing data to train,validation and test sets.
ph2- Download the ph2 dataset from this link and extract it then Run Prepare_ph2.py for data preperation and dividing data to train,validation and test sets.
Follow step 3 and 4 for model traing and performance estimation. For ph2 dataset you need to first train the model with ISIC 2018 data set and then fine-tune the trained model using ph2 dataset.

Quick Overview

Diagram of the proposed Attention mechanism

Performance Evalution on the Skin Lesion Segmentation ISIC 2018

Methods	Year	F1-scores	Sensivity	Specificaty	Accuracy	PC	JS
Ronneberger and etc. all U-net	2015	0.647	0.708	0.964	0.890	0.779	0.549
Alom et. all Recurrent Residual U-net	2018	0.679	0.792	0.928	0.880	0.741	0.581
Oktay et. all Attention U-net	2018	0.665	0.717	0.967	0.897	0.787	0.566
Alom et. all R2U-Net	2018	0.691	0.726	0.971	0.904	0.822	0.592
Azad et. all BCDU-Net	2019	0.847	0.783	0.980	0.936	0.922	0.936
Asadi et. all MCGU-Net	2020	0.895	0.848	0.986	0.955	0.947	0.955
Azad et. all Attention Deeplabv3p	2020	0.912	0.885	0.988	0.964	..	0.964

Segmentation visualization

Model weights

You can download the learned weights for each dataset in the following table.

Dataset	Learned weights
ISIC 2018	Deeplabv3pa
ISIC 2017	Deeplabv3pa
Ph2	Deeplabv3pa

Query

All implementation done by Reza Azad. For any query please contact us for more information.

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