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Link zu diesem Datensatz | https://d-nb.info/1373605359 |
Titel | Intrapartum Ultrasound : MICCAI 2024 Grand Challenge, IUGC 2024, Held in Conjunction with MICCAI 2024, Marrakesh, Morocco, October 6, 2024, Proceedings / edited by Jieyun Bai, Yaosheng Lu |
Person(en) |
Bai, Jieyun (Herausgeber) Lu, Yaosheng (Herausgeber) |
Organisation(en) | SpringerLink (Online service) (Sonstige) |
Ausgabe | 1st ed. 2026 |
Verlag | Cham : Springer Nature Switzerland, Imprint: Springer |
Zeitliche Einordnung | Erscheinungsdatum: 2026 |
Umfang/Format | Online-Ressource, X, 126 p. 39 illus., 35 illus. in color. : online resource. |
Andere Ausgabe(n) |
Printed edition:: ISBN: 978-3-031-96317-9 Printed edition:: ISBN: 978-3-031-96319-3 |
Inhalt | Baseline Method at the Intrapartum Ultrasound Grand Challenge 2024 -- Classification and Segmentation of Intrapartum Ultrasound Images with Deep Learning Models -- Accurate Fetal Head Descent Assessment During Labor Using Video Swin Transformer and Wavelet-Based Multitask Learning for 2024 MICCAI Challenge IUGC -- DSSAU-Net:U-Shaped Hybrid Network for Pubic Symphysis and Fetal Head Segmentation -- Intrapartum Ultrasound Grand Challenge, MICCAI 2024 -- Multi-Frequency Attention Approach for Enhanced Ultrasound Image Segmentation -- Multi-Task Weakly Supervised Intrapartum Ultrasound Measurements -- Evaluation of the Video-based Network for Angle of Progression Measurement -- Estimation of Labor Progress Parameters on Intrapartum Transperineal Ultrasound Images via Multi-Path Refinement U-Net Combining Recurrent Residuals and Attention |
Persistent Identifier |
URN: urn:nbn:de:101:1-2508120406289.858290863702 DOI: 10.1007/978-3-031-96318-6 |
URL | https://doi.org/10.1007/978-3-031-96318-6 |
ISBN/Einband/Preis | 978-3-031-96318-6 |
Sprache(n) | Englisch (eng) |
Beziehungen | Lecture Notes in Computer Science ; 15689 |
DDC-Notation | 616.075 (maschinell ermittelte DDC-Kurznotation) |
Sachgruppe(n) | 610 Medizin, Gesundheit |
Online-Zugriff | Archivobjekt öffnen |
