Self-supervised contrastive learning with random walks for medical image segmentation with limited annotations

Author:

Fischer MarcORCID,Hepp Tobias,Gatidis Sergios,Yang Bin

Funder

Deutsche Forschungsgemeinschaft

Publisher

Elsevier BV

Subject

Computer Graphics and Computer-Aided Design,Health Informatics,Computer Vision and Pattern Recognition,Radiology, Nuclear Medicine and imaging,Radiological and Ultrasound Technology

Reference77 articles.

1. The medical segmentation decathlon. arXiv e-prints;Antonelli;ArXiv,2021

2. Assran, M., et al., 2021. Semi-supervised learning of visual features by non-parametrically predicting view assignments with support samples, in: Proc. IEEE/CVF Int. Conf. Comput. Vis., pp. 8443–8452.

3. Ayyad, A., Li, Y., Muaz, R., Albarqouni, S., Elhoseiny, M., 2021. Semi-supervised few-shot learning with prototypical random walks, 45–57.

4. Learning representations by maximizing mutual information across views;Bachman;Adv. Neural Inf. Process. Syst.,2019

5. Bai, W., et al., 2017. Semi-supervised learning for network-based cardiac mr image segmentation, in: Proc. Int. Conf. Med. Image Comput. Comput. Interv, Springer. pp. 253–260.

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