mqinghe / midss

MiDSS

1. Introduction

This repository contains the implementation of the paper Constructing and Exploring Intermediate Domains in Mixed Domain Semi-supervised Medical Image Segmentation

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" folder.

3. Train

code/train.py is the implementation of our method on the Prostate and Fundus dataset.

code/train_MNMS.py is the implementation of our method on the M&Ms dataset.

Modify the paths in lines 631 to 636 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/mnms" # the folder of mnms dataset

then simply run:

python train.py --dataset ... --lb_domain ... --lb_num ... --save_name ... --gpu 0

4. Test

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

Modify the paths in lines 249 to 254 of the code.

then simply run:

python test.py --dataset ... --save_name ... --gpu 0

5. Acknowledgement

This project is based on the code from the SSL4MIS project.

Thanks a lot for their great works.

midss's Issues

Dataset acquisition issues

Thank you very much for your efforts, can you provide the Fundu dataset, it's really hard to get and download the data for the various domains.

the preprocessed Fundus dataset

Hello, thank you very much for providing such excellent work. Would it be convenient for you to provide the preprocessed Fundus dataset？Thank you very much!

M&Ms dataset

Great work！ but I noticed that the source of the M&Ms dataset is not provided in the paper. Could you please provide the download link for the M&Ms dataset?

Data preprocessing

I'm very sorry to bother you again. Can you share the M&M preprocessed dataset?

preprocessed Prostate dataset

Hello author, I'm sorry to bother you again. Do you have a preprocessed Prostate dataset？Thank you very much!