MDCS

1. Introduction

My project is an open-source project based on the paper 'Enhancing Medical Image Segmentation with Collaborative and Contrastive Learning in Mixed-Domain Settings.

2. Dataset Construction

The dataset needs to be divided into two folders for training and testing. The training and testing data should be in the format of the "data_format" folder.

3. Train

code/train.py is the implementation of our method .

Modify the paths in lines 770 to 817 of the code.

if args.dataset == 'fundus':
    train_data_path='../../data/Fundus' # the folder of fundus dataset
elif args.dataset == 'prostate':
    train_data_path="../../data/ProstateSlice" # the folder of prostate dataset
elif args.dataset == 'MNMS':
    train_data_path="../../data/mnms" # the folder of M&Ms dataset
elif args.dataset == 'BUSI':
    train_data_path="../../data/Dataset_BUSI_with_GT" # the folder of BUSI dataset

then simply run:

python train.py

4. Test

To run the evaluation code, please update the path of the dataset in test.py:

Modify the paths in lines 248 to 283 of the code.

then simply run:

python test_generate_label.py

5. DataSets

Prostate with the extraction code: 4no2

Fundus

M&Ms with the extraction code: cdbs

BUSI

The Prostate and M&Ms datasets have undergone preprocessing in our work, with the original data sourced from prostate and M&Ms

Thanks a lot for their great works.