HiCo: Hierarchical Contrastive Learning for Ultrasound Video Model Pretraining
This work addresses the challenge of improving generalization and adaptation in ultrasound diagnosis models for medical applications, representing an incremental advancement in domain-specific self-supervised learning.
The paper tackles the problem of learning transferable feature representations in self-supervised ultrasound video model pretraining by proposing HiCo, a hierarchical contrastive learning method that introduces peer-level and cross-level semantic alignment, resulting in favorable results over state-of-the-art approaches on five datasets.
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.