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Foundation Models for General Medical AI Second International Workshop, MedAGI 2024, Held in Conjunction with MICCAI 2024, Marrakesh, Morocco, October 6, 2024, Proceedings /

This book constitutes the refereed proceedings from the Second International Workshop on Foundation Models for General Medical AI, MedAGI 2024, held in conjunction with the 27th International conference on Medical Image Computing and Computer Assisted Intervention, MICCAI 2024, in Marrakesh, Morocco...

Повний опис

Збережено в:
Бібліографічні деталі
Співавтор: SpringerLink (Online service)
Інші автори: Deng, Zhongying (Редактор), Shen, Yiqing (Редактор), Kim, Hyunwoo J. (Редактор), Jeong, Won-Ki (Редактор), Aviles-Rivero, Angelica I. (Редактор), He, Junjun (Редактор), Zhang, Shaoting (Редактор)
Формат: Електронний ресурс eКнига
Мова:Англійська
Опубліковано: Cham : Springer Nature Switzerland : Imprint: Springer, 2025.
Редагування:1st ed. 2025.
Серія:Lecture Notes in Computer Science, 15184
Предмети:
Image processing > Digital techniques.
Computer vision.
Imaging systems in biology.
Computer Imaging, Vision, Pattern Recognition and Graphics.
Biological Imaging.
Онлайн доступ:https://doi.org/10.1007/978-3-031-73471-7
Теги: Додати тег
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Зміст:
  • -- 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.

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