A Federated Learning Framework for Stenosis Detection
Author:
Publisher
Springer Nature Switzerland
Link
https://link.springer.com/content/pdf/10.1007/978-3-031-51026-7_19
Reference24 articles.
1. Cong, C., Kato, Y., Vasconcellos, H.D., Lima, J., Venkatesh, B.: Automated stenosis detection and classification in X-ray angiography using deep neural network. In: 2019 IEEE International Conference on Bioinformatics and Biomedicine, pp. 1301–1308. IEEE (2019)
2. Danilov, V.V., et al.: Real-time coronary artery stenosis detection based on modern neural networks. Sci. Rep. 11(1), 1–13 (2021)
3. Fiorentino, M.C., Villani, F.P., Di Cosmo, M., Frontoni, E., Moccia, S.: A review on deep-learning algorithms for fetal ultrasound-image analysis. Med. Image Anal. 83, 102629 (2022)
4. Han, T., et al.: Coronary artery stenosis detection via proposal-shifted spatial-temporal transformer in X-ray angiography. Comput. Biol. Med. 153, 106546 (2023)
5. Lawton, J.S., et al.: 2021 ACC/AHA/SCAI guideline for coronary artery revascularization: executive summary: a report of the American College of Cardiology/American Heart Association Joint Committee on Clinical Practice Guidelines. Circulation 145(3), e4–e17 (2022)
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