Abstract
The scarcity of richly annotated medical images is limiting supervised deep learning based solutions to medical image analysis tasks, such as localizing discriminatory radiomic disease signatures. Therefore, it is desirable to leverage unsupervised and weakly supervised models. Most recent weakly supervised localization methods apply attention maps or region proposals in a multiple instance learning formulation. While attention maps can be noisy, leading to erroneously highlighted regions, it is not simple to decide on an optimal window/bag size for multiple instance learning approaches. In this paper, we propose a learned spatial masking mechanism to filter out irrelevant background signals from attention maps. The proposed method minimizes mutual information between a masked variational representation and the input while maximizing the information between the masked representation and class labels. This results in more accurate localization of discriminatory regions. We tested the proposed model on the ChestX-ray8 dataset to localize pneumonia from chest X-ray images without using any pixel-level or bounding-box annotations.
Abstract (translated)
大量注释医学图像的缺乏限制了基于深度学习的医学图像分析任务的监督解决方案，例如定位歧视性放射性疾病特征。因此，最好利用无监督和弱监督的模型。最新的弱监督本地化方法在多实例学习公式中应用注意力图或区域建议。虽然注意力图可能会发出噪音，导致错误的高亮区域，但要为多实例学习方法确定最佳窗口/包大小并不容易。在本文中，我们提出了一个学习的空间掩蔽机制，从注意图中过滤出不相关的背景信号。该方法在最大化屏蔽表示和类标签之间的信息的同时，最小化了屏蔽变分表示和输入之间的相互信息。这使得歧视性区域的定位更加准确。我们在ChestX-Ray8数据集上测试了所提议的模型，以便在不使用任何像素级或边界框注释的情况下从胸部X光图像中定位肺炎。
URL
https://arxiv.org/abs/1903.11741
