SegICL: A Multimodal In-context Learning Framework for Enhanced Segmentation in Medical Imaging

no code implementations • 25 Mar 2024 • Lingdong Shen, Fangxin Shang, Xiaoshuang Huang, Yehui Yang, Haifeng Huang, Shiming Xiang

Universal segmentation models is a solution, which aim to generalize across the diverse modality of medical images, yet their effectiveness often diminishes when applied to OOD data modalities and tasks, requiring intricate fine-tuning of model for optimal performance.

Few-Shot Learning Image Segmentation +4