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Attention Residual Learning for Skin Lesion Classification(IEEE Transactions on Medical Imaging )

Introduction

This work is an unofficial code implemented by Pytorch. Some issues are different with original paper:

  • We don't employed additional dermoscopy images which is collected in ISIC Archive.
  • The data augment isn't same with original paper but the performance of our work is approximative.

Preparation

First of all, clone the code

https://github.com/Vipermdl/ARL

Then, create a folder for dataset:

cd ARL && mkdir data

Note: We currently only support ISIC 2017. To make things easy, we provide simple dataset loader that inherits torch.utils.data.Dataset making it fully compatible with the torchvision.datasets API.

A soft-link is recommended.

ln -s /path/to/isic2017 ./data/isic2017

Then, we sliced the images for patches to train:

python generate_patch_images.py

It should have this basic structure

$ISIC2017/
$ISIC2017/ISIC-2017_Test_v2_Data/
$ISIC2017/ISIC-2017_Training_Data/
$ISIC2017/ISIC-2017_Validation_Data/
$ISIC2017/ISIC-2017_Training_Data_Patch/
$ISIC2017/ISIC-2017_Test_v2_Part3_GroundTruth.csv/
$ISIC2017/ISIC-2017_Training_Part3_GroundTruth.csv/
$ISIC2017/ISIC-2017_Validation_Part3_GroundTruth.csv/
$ISIC2017/ISIC-2017_Training_Part3_GroundTruth_patch.csv/

prerequisites

  • Python 3.6
  • Pytorch 0.4.0 or higher
  • CUDA 8.0 or higher
  • Install all the python dependencies using pip: pip install -r requirements.txt

Train

python train_mel.py

Test

python predict2017_mel.py

Performance Comparision

We re-implemented with ARLNet50 for the task of Melanoma Classification:

Work params AUC ACC Sensitivity Specificity
Original paper 2.3 0.875 0.850 0.658 0.96
Our work 2.35 0.872 0.850 0.487 0.937

Below is AUC figure:

Authorship

This repository is produced by Dongliang Ma, if you have any question, please contact with me.