- 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.