Bootstrapping Chest CT Image Understanding by Distilling Knowledge from X-ray Expert Models

arXiv:2404.04936v1 Announce Type: new
Abstract: Radiologists highly desire fully automated versatile AI for medical imaging interpretation. However, the lack of extensively annotated large-scale multi-disease datasets has hindered the achievement of this goal. In this paper, we explore the feasibility of leveraging language as a naturally high-quality supervision for chest CT imaging. In light of the limited availability of image-report pairs, we bootstrap the understanding of 3D chest CT images by distilling chest-related diagnostic knowledge from an extensively pre-trained 2D X-ray …

abstract achievement arxiv automated bootstrapping cs.cv datasets disease expert explore however image imaging interpretation knowledge language medical medical imaging paper quality ray scale supervision type understanding x-ray