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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:  9783032051844 
    EAN-Code: 
    9783032051844 
    Verlag:  Springer International Publishing 
    Einband:  Kartoniert  
    Sprache:  English  
    Dimensionen:  H 235 mm / B 155 mm / D  
    Seiten:  695 
    Illustration:  LI, 695 p. 203 illus., 200 illus. in color., schwarz-weiss Illustrationen, farbige 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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