C-DARL: Contrastive diffusion adversarial representation learning for label-free blood vessel segmentation

Author:

Kim BoahORCID,Oh YujinORCID,Wood Bradford J.,Summers Ronald M.ORCID,Ye Jong ChulORCID

Funder

National Research Foundation of Korea

National Institutes of Health

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

Reference46 articles.

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3. Caron, M., Touvron, H., Misra, I., Jégou, H., Mairal, J., Bojanowski, P., Joulin, A., 2021. Emerging Properties in Self-Supervised Vision Transformers. In: Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV). pp. 9650–9660.

4. Automatic segmentation of coronary arteries in X-ray angiograms using multiscale analysis and artificial neural networks;Cervantes-Sanchez;Appl. Sci.,2019

5. A simple framework for contrastive learning of visual representations;Chen,2020

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