Semi-Supervised Skin Lesion Segmentation under Dual Mask Ensemble with Feature Discrepancy Co-Training

Introduction

Official Code by Thanh-Huy Nguyen and Hoang-Thien Nguyen et.al.

Our Proposed Framework

Requirements

This repository is based on PyTorch 1.8.0, CUDA 11.1, and Python 3.6.13. All experiments in our paper were conducted on NVIDIA GeForce RTX 3090ti GPU with an identical experimental setting.

Data Preparation

Folder Organization

|-data
    |-isic2018
        |-images
        |-labels
        |-fold*.txt
    |-ham10000
        |-images
        |-labels
        |-fold*.txt

Download Dataset Link

ISIC2018: [Link](https://challenge.isic-archive.com/data/#2018) - (Task 1: Train + Val + Test)
HAM10000: Image ([Link](https://challenge.isic-archive.com/data/#2018) - Task 3) + Label ([Link](https://www.kaggle.com/datasets/tschandl/ham10000-lesion-segmentations))

Set up ISIC2018

Move all extracted images into 'images' folder and labels into 'labels' folder
Use the function process_isic2018 function in process_resize.py to resize images and labels and convert it to .npy file
Move processed folder into data/isic2018

Set up HAM10000

Move all extracted images into 'images' folder and labels into 'labels' folder
Use the function process_isic2018 function in process_resize.py to resize images and labels and convert it to .npy file
Move processed folder into data/ham10000

Training Usage

To config the dataset and hyperparameters,

Modify Configs/multi_train.local.yml

To train a supervised model,

python ./code/train_sup.py --dataset isic2018 #for ISIC 2018
python ./code/train_sup.py --dataset ham10000 #for HAM10000

To train a semi-supervised model,

python ./code/train_dme-fd.py --dataset isic2018 #for ISIC 2018
python ./code/train_dme-fd.py --dataset ham10000 #for HAM10000

Questions

If you have any questions, welcome contact us at 'thanh-huy_nguyen@etu.u-bourgogne.fr' (Huy) or 'n21dccn080@student.ptithcm.edu.vn' (Thien)