Παραπομπή σε μορφή APA (7η εκδ.)

Sanaat, A., Boehringer, A., Ghavabesh, A., Shiri, I., Salimi, Y., Arabi, H., & Zaidi, H. (2021). Deep-PVC: A Deep Learning Model for Synthesizing Full-Dose Partial Volume Corrected PET Images from Low-Dose Images. 2021 IEEE Nuclear Science Symposium and Medical Imaging Conference (NSS/MIC), Nuclear Science Symposium and Medical Imaging Conference (NSS/MIC), 2021 IEEE, 1. https://doi.org/10.1109/NSS/MIC44867.2021.9875501

Παραπομπή σε μορφή Chicago (17η εκδ.)

Sanaat, A., A. Boehringer, A. Ghavabesh, I. Shiri, Y. Salimi, H. Arabi, και H. Zaidi. "Deep-PVC: A Deep Learning Model for Synthesizing Full-Dose Partial Volume Corrected PET Images from Low-Dose Images." 2021 IEEE Nuclear Science Symposium and Medical Imaging Conference (NSS/MIC), Nuclear Science Symposium and Medical Imaging Conference (NSS/MIC), 2021 IEEE 2021: 1. https://doi.org/10.1109/NSS/MIC44867.2021.9875501.

Παραπομπή σε μορφή MLA (9th εκδ.)

Sanaat, A., et al. "Deep-PVC: A Deep Learning Model for Synthesizing Full-Dose Partial Volume Corrected PET Images from Low-Dose Images." 2021 IEEE Nuclear Science Symposium and Medical Imaging Conference (NSS/MIC), Nuclear Science Symposium and Medical Imaging Conference (NSS/MIC), 2021 IEEE, 2021, p. 1, https://doi.org/10.1109/NSS/MIC44867.2021.9875501.

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