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.
