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.
- 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).
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
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.
| 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 |
You can download the learned weights for each dataset in the following table.
| Dataset | Learned weights |
|---|---|
| ISIC 2018 | Deeplabv3pa |
| ISIC 2017 | Deeplabv3pa |
| Ph2 | Deeplabv3pa |
All implementation done by Reza Azad. For any query please contact us for more information.
rezazad68@gmail.com