Complementary Network with Adaptive Receptive Fields for Melanoma Segmentation

by Xiaoqing Guo, Zhen Chen, Yixuan Yuan.

Summary:

Intoduction:

This repository is for our ISBI2020 paper "Complementary Network with Adaptive Receptive Fields for Melanoma Segmentation", which aims to solve the hole (Fig. 1 (a)) and shrink (Fig. 1 (b)) problem in predictions. The relatively low contrast between melanoma and non-melanoma regions confuses the network and causes the appearance of holes. The fuzzy boundaries lead to the shrinking prediction and further decrease the sensitivity of prediction.

Fig. 1: Illustrations of (a) hole problem, (b) shrink problem. Each group includes the original image, ground truth and prediction of U-Net from left to right.

Framework:

Usage:

Requirement:

Tensorflow 1.4 Python 3.5

Preprocessing:

Clone the repository:

git clone https://github.com/Guo-Xiaoqing/Skin-Seg.git
cd Skin-Seg

Make tfrecord

• Please remember to augment dataset before make txt files

• Use "txt.py" to split training data and testing data. The generated txt files are showed in folder "./txt/".

• "make_tfrecords.py" is used to make tfrecord format data, which could be stored in folder "./skin2018/tfrecord/".

Train the model:

sh ./script/train_dml_mobilenet_on_market.sh

Test the model:

sh ./script/evaluate_dml_mobilenet_on_market.sh

Results:

Each row includes the original image, dilated rate map, predictions and ground truth from left to right. Note that red in heat map denotes a larger receptive field.

Examples of complementary network results in comparison with other methods. The ground truth is denoted in black. Results of \cite{ronneberger2015u}, \cite{sarker2018slsdeep}, \cite{yuan2017improving} and ours are denoted in blue, cyan, green, and red, respectively.

Citation:

If you found this repository helpful for your research, please cite our paper:

@inproceedings{guo2020complementary,
  title={Complementary network with adaptive receptive fields for melanoma segmentation},
  author={Guo, Xiaoqing and Chen, Zhen and Yuan, Yixuan},
  booktitle={2020 IEEE 17th International Symposium on Biomedical Imaging (ISBI)},
  pages={2010--2013},
  year={2020},
  organization={IEEE}
}

Questions:

Please contact "[email protected]"