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Herausgeber: 
  • James C. Gee
  • Daniel C. Alexander
  • Polina Golland
  • Archana Venkataraman
  • Jinah Park
  • Carole H. Sudre
  • Jaesung Hong
  • Juan Eugenio Iglesias
  • Jong Hyo Kim
  • Medical Image Computing and Computer Assisted Intervention – MICCAI 2025: 28th International Conference, Daejeon, South Korea, September 23–27, 2025,  
     

    (Buch)
    Dieser Artikel gilt, aufgrund seiner Grösse, beim Versand als 3 Artikel!


    Übersicht

    Auf mobile öffnen
     
    Lieferstatus:   Auf Bestellung (Lieferzeit unbekannt)
    Veröffentlichung:  September 2025  
    Genre:  EDV / Informatik 
     
    angewandte informatik / Animation / Applied computing / Biomedical Engineering and Bioengineering / Biomedizinische Technik / Computational biology / Computer and Information Systems Applications / Computer Application in Social and Behavioral Sciences
    ISBN:  9783032049834 
    EAN-Code: 
    9783032049834 
    Verlag:  Springer International Publishing 
    Einband:  Kartoniert  
    Sprache:  English  
    Dimensionen:  H 235 mm / B 155 mm / D  
    Seiten:  663 
    Illustration:  LI, 663 p. 202 illus., 198 illus. in color., farbige Illustrationen, schwarz-weiss Illustrationen 
    Zus. Info:  EUDR exemption - product or manufacturing materials placed on the market prior to 31.12.2025. 
    Bewertung: Titel bewerten / Meinung schreiben
    Inhalt:

    The 16-volume set LNCS 15960 - 15975 constitutes the refereed proceedings of the 28th International Conference on Medical Image Computing and Computer Assisted Intervention, MICCAI 2025, which took place in Daejeon, South Korea, during September 23–27, 2025.

    The total of 1027 papers included in the proceedings was carefully reviewed and selected from 3447 submissions. They were organized in topical parts as follows:

    Part I, LNCS Volume 15960: Multimodal Fusion and Contextual Reasoning in Medical Imaging

    Part II, LNCS Volume 15961: Surgical Navigation, Scene Understanding, and Video Modeling

    Part III, LNCS Volume 15962: Learning and Augmented Reality for Surgical and Endoscopic Applications (I)

    Part IV, LNCS Volume 15963: Learning and Augmented Reality for Surgical and Endoscopic Applications (II)

    Part V, LNCS Volume 15964: Graph-Based Methods in Medical Imaging

    Part VI, LNCS Volume 15965: Datasets and Methods for Image Quality Enhancement

    Part VII, LNCS Volume 15966: Trustworthy and Responsible AI for Medical Imaging

    Part VIII, LNCS Volume 15967: Multimodal Learning for Diagnosis, Risk Prediction, and Survival Analysis

    Part IX, LNCS Volume 15968: Core Techniques in Medical Imaging: Segmentation, Registration, Synthesis, Reconstruction, and Other Emerging Methods (I)

    Part X, LNCS Volume 15969: Core Techniques in Medical Imaging: Segmentation, Registration, Synthesis, Reconstruction, and Other Emerging Methods (II)

    Part XI, LNCS Volume 15970: Core Techniques in Medical Imaging: Segmentation, Registration, Synthesis, Reconstruction, and Other Emerging Methods (III)

    Part XII, LNCS Volume 15971: Core Techniques in Medical Imaging: Segmentation, Registration, Synthesis, Reconstruction, and Other Emerging Methods (IV)

    Part XIII, LNCS Volume 15972: Adapting Foundation Models for Medical Imaging: LLMs, VLMs, and Cross-Domain Generalization (I)

    Part XIV, LNCS Volume 15973: Adapting Foundation Models for Medical Imaging: LLMs, VLMs, and Cross-Domain Generalization (II)

    Part XV, LNCS Volume 15974: Adapting Foundation Models for Medical Imaging: LLMs, VLMs, and Cross-Domain Generalization (III)

    Part XVI, LNCS Volume 15975: Statistical Techniques in Medical Imaging: Causality, Imputation, Weak Supervision, and Other Methods

     

      



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