论文标题
具有跨注意网络的多标签胸部疾病图像分类
Multi-label Thoracic Disease Image Classification with Cross-Attention Networks
论文作者
论文摘要
放射学图像的自动疾病分类已成为支持临床诊断和治疗计划的一种有前途的技术。与通用图像分类任务不同，现实世界放射学图像分类任务明显更具挑战性，因为收集标记数据本质上是多标签的训练数据要昂贵得多；更认真的简单课程样本通常主导；培训数据在实践中也存在高度的班级失控问题。为了克服这些挑战，在本文中，我们提出了一种新的跨意义网络（CAN）方案，用于从胸部X射线图像中进行自动化的胸病疾病分类，该图像可以通过图像级别的注释有效地从数据中挖掘出更有意义的表示，以通过图像级别的注释来提高性能。我们还设计了一个新的损失函数，超出了跨凝性损失，以帮助跨注意过程，并能够克服每个类别中类和易于主导的样本之间的失衡。所提出的方法可实现最先进的结果。
Automated disease classification of radiology images has been emerging as a promising technique to support clinical diagnosis and treatment planning. Unlike generic image classification tasks, a real-world radiology image classification task is significantly more challenging as it is far more expensive to collect the training data where the labeled data is in nature multi-label; and more seriously samples from easy classes often dominate; training data is highly class-imbalanced problem exists in practice as well. To overcome these challenges, in this paper, we propose a novel scheme of Cross-Attention Networks (CAN) for automated thoracic disease classification from chest x-ray images, which can effectively excavate more meaningful representation from data to boost the performance through cross-attention by only image-level annotations. We also design a new loss function that beyond cross-entropy loss to help cross-attention process and is able to overcome the imbalance between classes and easy-dominated samples within each class. The proposed method achieves state-of-the-art results.
