论文标题
逐渐在乳房超声图像中应用弱监督和主动学习以进行质量检测
Gradually Applying Weakly Supervised and Active Learning for Mass Detection in Breast Ultrasound Images
论文作者
论文摘要
我们提出了一种在乳房超声图像的对象检测任务中有效利用弱注释的图像数据的方法。考虑到一个问题设置，一个小的，强烈的注释数据集和一个没有边界框信息的大型，弱注释的数据集，训练对象检测模型成为一个非平凡的问题。我们建议在两个阶段对象检测模型中处理弱注释图像的效果的受控权重。我们还提出了一种随后的主动学习方案，用于使用训练有素的模型安全地分配弱注释的图像作为强烈注释。实验结果表明，通过分配正确控制的重量，正确定位（CORLOC）度量的正确定位（CORLOC）度量增加了24 \％。在训练模型后进行主动学习表明Corloc的增加。我们在斯坦福狗数据集上测试了提出的方法，以确保可以应用于一般情况，在这种情况下，强有力的注释不足以获得类似结果。提出的方法表明，通过较少的注释努力，可以实现更高的性能。
We propose a method for effectively utilizing weakly annotated image data in an object detection tasks of breast ultrasound images. Given the problem setting where a small, strongly annotated dataset and a large, weakly annotated dataset with no bounding box information are available, training an object detection model becomes a non-trivial problem. We suggest a controlled weight for handling the effect of weakly annotated images in a two stage object detection model. We~also present a subsequent active learning scheme for safely assigning weakly annotated images a strong annotation using the trained model. Experimental results showed a 24\% point increase in correct localization (CorLoc) measure, which is the ratio of correctly localized and classified images, by assigning the properly controlled weight. Performing active learning after a model is trained showed an additional increase in CorLoc. We tested the proposed method on the Stanford Dog datasets to assure that it can be applied to general cases, where strong annotations are insufficient to obtain resembling results. The presented method showed that higher performance is achievable with lesser annotation effort.
