Abstract This study presents a novel deep learning architecture for multi-class classification and localization of abnormalities in medical imaging illustrated through experiments on mammograms. The proposed network combines two learning branches. One branch is for region classification with a newly added normal-region class. Second branch is region detection branch for ranking regions relative to one another. Our method enables detection of abnormalities at full mammogram resolution for both weakly and semi-supervised settings. A novel objective function allows for the incorporation of local annotations into the model. We present the impact of our schemes on several performance measures for classification and localization, to evaluate the cost effectiveness of the lesion annotation effort. Our evaluation was primarily conducted over a large multi-center mammography dataset of ~ 3,000 mammograms with various findings. The results for weakly supervised learning showed significant improvement compared to previous approaches. We show that the time consuming local annotations involved in supervised learning can be addressed by a weakly supervised method that can leverage a subset of locally annotated data. Weakly and semi-supervised methods coupled with detection can produce a cost effective and explainable model to be adopted by radiologists in the field.

中文翻译：

---

基于深度双分支网络的医学影像弱监督和半监督检测

摘要 本研究提出了一种新的深度学习架构，用于医学成像中异常的多类分类和定位，通过乳房 X 光照片实验说明。提议的网络结合了两个学习分支。一个分支用于具有新添加的正常区域类的区域分类。第二个分支是区域检测分支，用于对区域进行相对排序。我们的方法能够在弱监督和半监督设置下以全乳房 X 光照片分辨率检测异常。新的目标函数允许将局部注释合并到模型中。我们展示了我们的方案对分类和定位的几种性能指标的影响，以评估病变注释工作的成本效益。我们的评估主要是在一个包含约 3,000 个乳房 X 光照片的大型多中心乳房 X 光摄影数据集上进行的，其中包含各种发现。与以前的方法相比，弱监督学习的结果显示出显着的改进。我们表明，监督学习中涉及的耗时的本地注释可以通过弱监督方法来解决，该方法可以利用本地注释数据的子集。弱监督和半监督方法与检测相结合可以产生一个成本有效且可解释的模型，供该领域的放射科医生采用。我们表明，监督学习中涉及的耗时的本地注释可以通过弱监督方法来解决，该方法可以利用本地注释数据的子集。弱监督和半监督方法与检测相结合可以产生一个成本有效且可解释的模型，供该领域的放射科医生采用。我们表明，监督学习中涉及的耗时的本地注释可以通过弱监督方法来解决，该方法可以利用本地注释数据的子集。弱监督和半监督方法与检测相结合可以产生一个成本有效且可解释的模型，供该领域的放射科医生采用。