  The self-supervised ultrasound (US) video model pretraining can use a small
amount of labeled data to achieve one of the most promising results on US
diagnosis. However, it does not take full advantage of multi-level knowledge
for learning deep neural networks (DNNs), and thus is difficult to learn
transferable feature representations. This work proposes a hierarchical
contrastive learning (HiCo) method to improve the transferability for the US
video model pretraining. HiCo introduces both peer-level semantic alignment and
cross-level semantic alignment to facilitate the interaction between different
semantic levels, which can effectively accelerate the convergence speed,
leading to better generalization and adaptation of the learned model.
Additionally, a softened objective function is implemented by smoothing the
hard labels, which can alleviate the negative effect caused by local
similarities of images between different classes. Experiments with HiCo on five
datasets demonstrate its favorable results over state-of-the-art approaches.
The source code of this work is publicly available at
https://github.com/983632847/HiCo.
