An Attention-based Semi-supervised Neural Network for Thyroid Nodules Segmentation

Image segmentation based on deep learning has greatly promoted the development of the field of computer-aided diagnosis. However, the large scale medical annotation of ground truth is so difficult that it directly affects the performance of existing segmentation models. In this work, an Attention based Semi-supervised Neural Network is proposed, which can complete end-to-end segmentation task of thyroid ultrasound image with weakly annotated classification data and a small amount of fully annotated segmentation data. Two kinds of attention modules are proposed to improve network performance through the trainable feedforward structure of bottom-up and top-down so as to suppress or activate the feature channels and image regions respectively. The experimental results show that when there is only 13% of fully annotated data, the Jaccard similarity coefficient of thyroid nodule segmentation is 74.91%, 4.97% higher than VGG-based semi-supervised model. The classification accuracy of benign and malignant is increased from 91.67% to 95.00%. Equally important, with the same number of fully annotated data, our model has better generalization than that of the supervised segmentation models.