  Well-annotated medical datasets enable deep neural networks (DNNs) to gain
strong power in extracting lesion-related features. Building such large and
well-designed medical datasets is costly due to the need for high-level
expertise. Model pre-training based on ImageNet is a common practice to gain
better generalization when the data amount is limited. However, it suffers from
the domain gap between natural and medical images. In this work, we pre-train
DNNs on ultrasound (US) domains instead of ImageNet to reduce the domain gap in
medical US applications. To learn US image representations based on unlabeled
US videos, we propose a novel meta-learning-based contrastive learning method,
namely Meta Ultrasound Contrastive Learning (Meta-USCL). To tackle the key
challenge of obtaining semantically consistent sample pairs for contrastive
learning, we present a positive pair generation module along with an automatic
sample weighting module based on meta-learning. Experimental results on
multiple computer-aided diagnosis (CAD) problems, including pneumonia
detection, breast cancer classification, and breast tumor segmentation, show
that the proposed self-supervised method reaches state-of-the-art (SOTA). The
codes are available at https://github.com/Schuture/Meta-USCL.
