Adapting Foundation Models for Annotation-Efficient Adnexal Mass Segmentation in Cine Images

ArXi:2604.08045v1 Announce Type: new Adnexal mass evaluation via ultrasound is a challenging clinical task, often hindered by subjective interpretation and significant inter-observer variability. While automated segmentation is a foundational step for quantitative risk assessment, traditional fully supervised convolutional architectures frequently require large amounts of pixel-level annotations and struggle with domain shifts common in medical imaging.