  Standard plane recognition plays an important role in prenatal ultrasound
(US) screening. Automatically recognizing the standard plane along with the
corresponding anatomical structures in US image can not only facilitate US
image interpretation but also improve diagnostic efficiency. In this study, we
build a novel multi-label learning (MLL) scheme to identify multiple standard
planes and corresponding anatomical structures of fetus simultaneously. Our
contribution is three-fold. First, we represent the class correlation by word
embeddings to capture the fine-grained semantic and latent statistical
concurrency. Second, we equip the MLL with a graph convolutional network to
explore the inner and outer relationship among categories. Third, we propose a
novel cluster relabel-based contrastive learning algorithm to encourage the
divergence among ambiguous classes. Extensive validation was performed on our
large in-house dataset. Our approach reports the highest accuracy as 90.25% for
standard planes labeling, 85.59% for planes and structures labeling and mAP as
94.63%. The proposed MLL scheme provides a novel perspective for standard plane
recognition and can be easily extended to other medical image classification
tasks.
