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:
- 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
- tensorflow backend
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
Prepare_ISIC2018.py for data preperation and dividing data to train,validation and test sets.
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
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
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.
Diagram of the proposed Attention mechanism
Performance Evalution on the Skin Lesion Segmentation ISIC 2018
|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|
You can download the learned weights for each dataset in the following table.
All implementation done by Reza Azad. For any query please contact us for more information.