The field of medical image analysis is witnessing significant advancements with the integration of foundation models and interactive segmentation techniques. Researchers are exploring the potential of foundation models to improve the generalization and robustness of medical image registration, segmentation, and diagnosis. These models, trained on large and diverse datasets, are demonstrating strong cross-task transferability and are being fine-tuned for specific applications such as cardiac CT segmentation and tumor tracking. Noteworthy papers in this area include:

• Improving Generalization of Medical Image Registration Foundation Model, which incorporates Sharpness-Aware Minimization to enhance the generalization and robustness of foundation models in medical image registration.
• MAIS: Memory-Attention for Interactive Segmentation, which introduces a novel Memory-Attention mechanism for interactive segmentation, enabling more efficient and accurate refinements.
• UniCAD: Efficient and Extendable Architecture for Multi-Task Computer-Aided Diagnosis System, which proposes a unified architecture for multi-task computer-aided diagnosis, leveraging pre-trained vision foundation models to handle both 2D and 3D medical images.