SURVS: A Swin-Unet and game theory-based unsupervised segmentation method for retinal vessel

Author:

Wang TianxiangORCID,Dai QunORCID

Funder

National Natural Science Foundation of China

National Key Research and Development Program of China

Publisher

Elsevier BV

Subject

Health Informatics,Computer Science Applications

Reference49 articles.

1. ROSE: a retinal OCT-angiography vessel segmentation dataset and new model;Ma;IEEE Trans. Med. Imag.,2021

2. Lightweight attention convolutional neural network for retinal vessel image segmentation;Li;IEEE Trans. Ind. Inf.,2021

3. FANet: a feedback attention network for improved biomedical image segmentation;Tomar;IEEE Transact. Neural Networks Learn. Syst.,2022

4. Retinal blood vessel extraction employing effective image features and combination of supervised and unsupervised machine learning methods;Hashemzadeh;Artif. Intell. Med.,2019

5. Blood vessel segmentation methodologies in retinal images - a survey;Fraz;Comput. Methods Progr. Biomed.,2012

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