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

arXiv:2403.16578v1 Announce Type: new
Abstract: Medical image segmentation models adapting to new tasks in a training-free manner through in-context learning is an exciting advancement. Universal segmentation models aim to generalize across the diverse modality of medical images, yet their effectiveness often diminishes when applied to out-of-distribution (OOD) data modalities and tasks, requiring intricate fine-tuning of model for optimal performance. For addressing this challenge, we introduce SegICL, a novel approach leveraging In-Context Learning (ICL) for image segmentation. Unlike existing methods, SegICL …

abstract advancement aim arxiv context cs.ai cs.cv data distribution diverse framework free image images imaging in-context learning medical medical imaging segmentation tasks through training type universal