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Classification and Detection in Mammograms with Weak Supervision via Dual Branch Deep Neural Net

2019-04-28 13:11:10
Ran Bakalo, Rami Ben-Ari, Jacob Goldberger

Abstract

The high cost of generating expert annotations, poses a strong limitation for supervised machine learning methods in medical imaging. Weakly supervised methods may provide a solution to this tangle. In this study, we propose a novel deep learning architecture for multi-class classification of mammograms according to the severity of their containing anomalies, having only a global tag over the image. The suggested scheme further allows localization of the different types of findings in full resolution. The new scheme contains a dual branch network that combines region-level classification with region ranking. We evaluate our method on a large multi-center mammography dataset including $\sim$3,000 mammograms with various anomalies and demonstrate the advantages of the proposed method over a previous weakly-supervised strategy.

Abstract (translated)

专家注释的生成成本高,对医学成像中的监控机器学习方法提出了极大的限制。弱监督方法可以解决这种纠结。在这项研究中,我们提出了一种新的深度学习架构,根据包含异常的严重程度对乳房X光片进行多类别分类,在图像上只有一个全局标记。建议的方案进一步允许将不同类型的发现以完全分辨率定位。新方案包含一个区域级分类和区域排序相结合的双分支网络。我们在一个大型多中心乳房摄影数据集上评估了我们的方法,其中包括具有各种异常情况的.sim$3000乳房摄影,并证明了所提出的方法相对于以前的弱监督策略的优势。

URL

https://arxiv.org/abs/1904.12319

PDF

https://arxiv.org/pdf/1904.12319.pdf


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