Description

Book Synopsis

.- 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.

Foundation Models for General Medical AI

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    A Paperback by Zhongying Deng

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      Publisher: Springer
      Publication Date: Publication Date: 03/10/2024
      ISBN13: 9783031734700, 978-3031734700
      ISBN10:
      Also in:
      Computing Image processing

      Description

      Book Synopsis

      .- 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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