  Self-supervised learning has proven to be an effective way to learn
representations in domains where annotated labels are scarce, such as medical
imaging. A widely adopted framework for this purpose is contrastive learning
and it has been applied to different scenarios. This paper seeks to advance our
understanding of the contrastive learning framework by exploring a novel
perspective: employing multi-organ datasets for pre-training models tailored to
specific organ-related target tasks. More specifically, our target task is
breast tumour segmentation in ultrasound images. The pre-training datasets
include ultrasound images from other organs, such as the lungs and heart, and
large datasets of natural images. Our results show that conventional
contrastive learning pre-training improves performance compared to supervised
baseline approaches. Furthermore, our pre-trained models achieve comparable
performance when fine-tuned with only half of the available labelled data. Our
findings also show the advantages of pre-training on diverse organ data for
improving performance in the downstream task.
