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

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

Extensive experimental validation of SegICL demonstrates a positive correlation between the number of prompt samples and segmentation performance on OOD modalities and tasks.

Image Segmentation In-Context Learning +3