This repository contains the official implementation of our paper titled "MFSNet: A Multi Focus Segmentation Network for Skin Lesion Segmentation" published in Pattern Recognition, Elsevier.
[Preprint]
To run the script for inpainting, run the following using the command prompt:
python inpaint.py --root "D:/inputs/" --destination "D:/images/"
Follow the directory structure as follows:
+-- data
| +-- .
| +-- train
| | +-- images
| | +-- masks
| +-- test
| | +-- images
| | +-- masks
+-- train.py
+-- test.py
Run the following to train the MFSNet network:
python train.py --train_path "data/train"
Other available hyperparameters for training are as follows:
--epoch: Number of epochs of training. Default = 100--lr: Learning Rate. Default = 1e-4--batchsize: Batch Size. Default = 20--trainsize: Size of Training images (to be resized). Default = 352--clip: Gradient Clipping Margin. Default = 0.5--decay_rate: Learning rate decay. Default = 0.05--decay_epoch: Number of epochs after which Learning Rate needs to decay. Default = 25
After the training is complete, run the following to generate the predictions on the test images:
python test.py --test_path "data/test"
Run eval/main.m using MATLAB on the ground truth images and the predicted masks, to get the evaluation measures.
If you find this repository useful, please cite our work:
@article{basak2022mfsnet,
title={MFSNet: A Multi Focus Segmentation Network for Skin Lesion Segmentation},
author={Basak, Hritam and Kundu, Rohit and Sarkar, Ram},
journal={Pattern Recognition},
pages={108673},
year={2022},
publisher={Elsevier}
}