Anatomy-Aware Contrastive Representation Learning for Fetal Ultrasound.

Zeyu Fu, Jianbo Jiao, Robail Yasrab, Lior Drukker, Aris T Papageorghiou, J Alison Noble
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Abstract

Self-supervised contrastive representation learning offers the advantage of learning meaningful visual representations from unlabeled medical datasets for transfer learning. However, applying current contrastive learning approaches to medical data without considering its domain-specific anatomical characteristics may lead to visual representations that are inconsistent in appearance and semantics. In this paper, we propose to improve visual representations of medical images via anatomy-aware contrastive learning (AWCL), which incorporates anatomy information to augment the positive/negative pair sampling in a contrastive learning manner. The proposed approach is demonstrated for automated fetal ultrasound imaging tasks, enabling the positive pairs from the same or different ultrasound scans that are anatomically similar to be pulled together and thus improving the representation learning. We empirically investigate the effect of inclusion of anatomy information with coarse- and fine-grained granularity, for contrastive learning and find that learning with fine-grained anatomy information which preserves intra-class difference is more effective than its counterpart. We also analyze the impact of anatomy ratio on our AWCL framework and find that using more distinct but anatomically similar samples to compose positive pairs results in better quality representations. Extensive experiments on a large-scale fetal ultrasound dataset demonstrate that our approach is effective for learning representations that transfer well to three clinical downstream tasks, and achieves superior performance compared to ImageNet supervised and the current state-of-the-art contrastive learning methods. In particular, AWCL outperforms ImageNet supervised method by 13.8% and state-of-the-art contrastive-based method by 7.1% on a cross-domain segmentation task. The code is available at https://github.com/JianboJiao/AWCL.

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胎儿超声的解剖学感知对比表示学习。
自监督对比表征学习提供了从未标记的医学数据集中学习有意义的视觉表征用于迁移学习的优势。然而,将当前的对比学习方法应用于医学数据而不考虑其特定领域的解剖特征,可能会导致视觉表示在外观和语义上不一致。在本文中,我们提出通过解剖学感知对比学习(AWCL)来改进医学图像的视觉表示,AWCL结合解剖学信息,以对比学习的方式增强正/负对采样。所提出的方法已被证明用于自动胎儿超声成像任务,使来自解剖相似的相同或不同超声扫描的阳性对能够被拉在一起,从而改进了表征学习。我们实证研究了包含粗粒度和细粒度解剖信息的效果,用于对比学习,并发现使用保留类内差异的细粒度解剖学信息进行学习比其对应信息更有效。我们还分析了解剖比例对AWCL框架的影响,发现使用更多不同但解剖相似的样本来组成正对会产生更好的质量表示。在大规模胎儿超声数据集上进行的大量实验表明,与ImageNet监督的方法和当前最先进的对比学习方法相比,我们的方法对于学习能够很好地转移到三个临床下游任务的表示是有效的,并实现了卓越的性能。特别是,在跨域分割任务中,AWCL比ImageNet监督的方法高13.8%,比最先进的基于对比的方法高7.1%。代码可在https://github.com/JianboJiao/AWCL.
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