ARANet: Adaptive Resolution Attention Network for Precise MRI-Based Segmentation and Quantification of Fetal Size and Amniotic Fluid Volume (Jun, 10.1007/s10278-025-01556-w, 2025)

Alkhadrawi, AM; Peña-Trujillo, V; Gallo-Bernal S.; Gee M.S.; Cobos, CEJ; Kim K.; Langarica S.; Kim Y.T.; Victoria, T; Do, S

Abstract

For this paper, the affiliations should be noted as below… Affiliation 2 should be: Department of Radiology, Massachusetts General Hospital, Harvard Medical School, Boston, MA 02114, USA Affiliation 3 should be: School of Transdisciplinary Innovations, Seoul National University, Seoul, 08826, South Korea Affiliation 4 should be: Department of Biomedical Science, College of Medicine, Seoul National University, Seoul, 03080, South Korea Affiliation 5 should be: Department of Electrical Engineering, Pontificia Universidad Catolica de Chile, Santiago, 8320165, Chile Affiliation 6 should be: KU-KIST Graduate School of Converging Science and Technology, Korea University, Seoul, 02841, South Korea Affiliation 7 should be: Kempner Institute, Harvard University, Boston, MA, 02134, USA © The Author(s) under exclusive licence to Society for Imaging Informatics in Medicine 2025.

Más información

Título según WOS: ARANet: Adaptive Resolution Attention Network for Precise MRI-Based Segmentation and Quantification of Fetal Size and Amniotic Fluid Volume (Jun, 10.1007/s10278-025-01556-w, 2025)
Título según SCOPUS: Correction: ARANet: Adaptive Resolution Attention Network for Precise MRI-Based Segmentation and Quantification of Fetal Size and Amniotic Fluid Volume (Journal of Imaging Informatics in Medicine, (2025), 10.1007/s10278-025-01556-w)
Título de la Revista: Journal of Imaging Informatics in Medicine
Editorial: Springer Nature
Fecha de publicación: 2025
Idioma: English
DOI:

10.1007/s10278-025-01727-9

Notas: ISI, SCOPUS