Region Mining of Fetal Head in Ultrasound Image Based on Weakly Supervised Annotations and Deep Learning

抄録

To locate the fetal head in ultrasound (US) images, this paper proposes a deep learning based method for weakly supervised learning from image-level annotation. We first modify and train fetal head classification models based on existing backbone structures, then adopt the feature maps and learned weights to visualize the high response areas of fetal head. In order to improve the localization accuracy, this paper further optimizes completeness of the salient area of the fetal head by adopting multiple feature maps from different feature levels. The final bounding box of the fetal head is obtained from mined regions through threshold. We evaluate both fetal head plane classification and weakly learned localization results in US images. In the experiments we compare several backbone structures and verify the effectiveness of the proposed method.