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| Link zu diesem Datensatz | https://d-nb.info/1343902029 |
| Art des Inhalts | Konferenzschrift |
| Titel | Foundation Models for General Medical AI : Second International Workshop, MedAGI 2024, Held in Conjunction with MICCAI 2024, Marrakesh, Morocco, October 6, 2024, Proceedings / edited by Zhongying Deng, Yiqing Shen, Hyunwoo J. Kim, Won-Ki Jeong, Angelica I. Aviles-Rivero, Junjun He, Shaoting Zhang |
| Person(en) |
Deng, Zhongying (Herausgeber) Shen, Yiqing (Herausgeber) Kim, Hyunwoo J. (Herausgeber) Jeong, Won-Ki (Herausgeber) Aviles-Rivero, Angelica I. (Herausgeber) He, Junjun (Herausgeber) Zhang, Shaoting (Herausgeber) |
| Organisation(en) | SpringerLink (Online service) (Sonstige) |
| Ausgabe | 1st ed. 2025 |
| Verlag | Cham : Springer Nature Switzerland, Imprint: Springer |
| Zeitliche Einordnung | Erscheinungsdatum: 2025 |
| Umfang/Format | Online-Ressource, X, 174 p. 56 illus., 52 illus. in color. : online resource. |
| Andere Ausgabe(n) |
Printed edition:: ISBN: 978-3-031-73470-0 Printed edition:: ISBN: 978-3-031-73472-4 |
| Inhalt | -- FastSAM-3DSlicer: A 3D-Slicer Extension for 3D Volumetric Segment Anything Model with Uncertainty Quantification. -- The Importance of Downstream Networks in Digital Pathology Foundation Models. -- Temporal-spatial Adaptation of Promptable SAM Enhance Accuracy and Generalizability of cine CMR Segmentation. -- Navigating Data Scarcity using Foundation Models: A Benchmark of Few-Shot and Zero-Shot Learning Approaches in Medical Imaging. -- AutoEncoder-Based Feature Transformation with Multiple Foundation Models in Computational Pathology. -- OSATTA: One-Shot Automatic Test Time Augmentation for Domain Adaptation. -- Automating MedSAM by Learning Prompts with Weak Few-Shot Supervision. -- SAT-Morph: Unsupervised Deformable Medical Image Registration using Vision Foundation Models with Anatomically Aware Text Prompt. -- Promptable Counterfactual Diffusion Model for Unified Brain Tumor Segmentation and Generation with MRIs. -- D- Rax: Domain-specific Radiologic assistant leveraging multi-modal data and eXpert model predictions. -- Optimal Prompting in SAM for Few-Shot and Weakly Supervised Medical Image Segmentation. -- UniCrossAdapter: Multimodal Adaptation of CLIP for Radiology Report Generation. -- TUMSyn: A Text-Guided Generalist model for Customized Multimodal MR Image Synthesis. -- SAMU: An Efficient and Promptable Foundation Model for Medical Image Segmentation. -- Anatomical Embedding-Based Training Method for Medical Image Segmentation Foundation Models. -- Boosting Vision-Language Models for Histopathology Classification: Predict all at once. -- MAGDA: Multi-agent guideline-driven diagnostic assistance |
| Persistent Identifier |
URN: urn:nbn:de:101:1-2410030426375.611526663149 DOI: 10.1007/978-3-031-73471-7 |
| URL | https://doi.org/10.1007/978-3-031-73471-7 |
| ISBN/Einband/Preis | 978-3-031-73471-7 |
| Sprache(n) | Englisch (eng) |
| Beziehungen | Lecture Notes in Computer Science ; 15184 |
| DDC-Notation | 616.075 (maschinell ermittelte DDC-Kurznotation) |
| Sachgruppe(n) | 610 Medizin, Gesundheit |
| Online-Zugriff | Archivobjekt öffnen |
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