Training-free image style alignment for self-adapting domain shift on handheld ultrasound devices

arXiv:2402.11211v1 Announce Type: cross
Abstract: Handheld ultrasound devices face usage limitations due to user inexperience and cannot benefit from supervised deep learning without extensive expert annotations. Moreover, the models trained on standard ultrasound device data are constrained by training data distribution and perform poorly when directly applied to handheld device data. In this study, we propose the Training-free Image Style Alignment (TISA) framework to align the style of handheld device data to those of standard devices. The proposed TISA can …

abstract alignment annotations arxiv benefit cs.cv data deep learning device data devices distribution domain eess.iv expert face free image limitations shift standard style training training data type usage