论文标题
为超声对比学习生成和加权在语义上一致的样本对
Generating and Weighting Semantically Consistent Sample Pairs for Ultrasound Contrastive Learning
论文作者
论文摘要
通知的医学数据集使深度神经网络（DNN）能够在提取与病变相关的功能方面获得强大的力量。由于需要高级专业知识，构建如此大型且设计良好的医疗数据集的代价很高。基于ImageNet的模型预训练是一种普遍的做法，可以在数据量受到限制时获得更好的概括。但是，它遭受了自然图像和医学图像之间的域间隙。在这项工作中，我们在超声（US）域而不是ImageNet上预先培训DNN，以减少美国医疗应用中的域间隙。为了学习基于未标记的美国视频的图像表示形式，我们提出了一种新型的基于元学习的对比学习方法，即元超声对比度学习（Meta-uscl）。为了应对获得对比度学习的语义一致样品对的关键挑战，我们提出了一个正面的生成模块以及基于元学习的自动样品加权模块。关于多个计算机辅助诊断（CAD）问题的实验结果，包括肺炎检测，乳腺癌分类和乳腺肿瘤分割，表明拟议的自我监督方法达到了最先进的方法（SOTA）。这些代码可在https://github.com/schuture/meta-uscl上找到。
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.
