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)
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 |