Semantic Segmentation Refiner for Ultrasound Applications with Zero-Shot Foundation Models

arXiv:2404.16325v1 Announce Type: new
Abstract: Despite the remarkable success of deep learning in medical imaging analysis, medical image segmentation remains challenging due to the scarcity of high-quality labeled images for supervision. Further, the significant domain gap between natural and medical images in general and ultrasound images in particular hinders fine-tuning models trained on natural images to the task at hand. In this work, we address the performance degradation of segmentation models in low-data regimes and propose a prompt-less segmentation method …

abstract analysis applications arxiv cs.ai cs.cv deep learning domain fine-tuning foundation gap general image images imaging medical medical imaging natural quality segmentation semantic success supervision type zero-shot