Min_Max_Similarity
A contrastive learning based semi-supervised segmentation network for medical image segmentation
This repository contains the implementation of a novel contrastive learning based semi-segmentation networks to segment the surgical tools.
Fig. 1. The architecture of Min-Max Similarity.
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The full paper is available: Min-Max Similarity
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The paper has been accepted by IEEE Transactions on Medical Imaging. The early access is available at Here.
Environment
conda install pytorch==1.8.0 torchvision==0.9.0 torchaudio==0.8.0 cudatoolkit=11.1 -c pytorch -c conda-forge
conda install opencv-python pillow numpy matplotlib
git clone https://github.com/AngeLouCN/Min_Max_Similarity
Data Preparation
We use five dataset to test its performance:
File structure
|-- data
| |-- kvasir
| | |-- train
| | | |--image
| | | |--mask
| | |-- test
| | | |--image
| | | |--mask
| |-- EndoVis17
| | |-- train
| | | |--image
| | | |--mask
| | |-- test
| | | |--image
| | | |--mask
......
You can also test on some other public medical image segmentation dataset with above file architecture
Usage
-
Training:
You can change the hyper-parameters like labeled ratio, leanring rate, and e.g. in train_mms.py, and directly run the code.
-
Testing:
You can change the dataset name in test.py and run the code.
Fig. 2. Visual comparison of our method with state-of-the-art models. Segmentation results are shown for 50% of labeled training data for Kvasir-instrument, EndVis’17, ART-NET and RoboTool, and 2.4% labeled training data for cochlear implant. From left to right are EndoVis’17, Kvasir-instrument, ART-NET, RoboTool, Cochlear implant and region of interest (ROI) of Cochlear implant.
Citation
@article{lou2023min,
title={Min-Max Similarity: A Contrastive Semi-Supervised Deep Learning Network for Surgical Tools Segmentation},
author={Lou, Ange and Tawfik, Kareem and Yao, Xing and Liu, Ziteng and Noble, Jack},
journal={IEEE Transactions on Medical Imaging},
year={2023},
publisher={IEEE}
}
Acknowledgement
Our code is based on the Duo-SegNet, we thank their excellent work and repository.
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