An Attentionbased Semisupervised Neural Network for Thyroid Nodules Segmentation Image segmentation based on deep learning has greatly promoted the development of the field of computeraided 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 Semisupervised Neural Network is proposed, which can complete endtoend 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 bottomup and topdown 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 VGGbased semisupervised 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.

FineGrained Thyroid Nodule Classification via MultiSemantic Attention Network Thyroid nodule classification in ultrasound images has gained great momentum based on deep convolutional neural networks in recent years. Nevertheless, it is still challenging to intelligently classify the finegrained thyroid nodules, which is significant for the subsequent clinical treatments. The difficulties mainly stem from four aspects: few finegrained training dataset, highlyvariable appearances of intraclass nodules, overallsimilar characteristics of interclass nodules, and the low resolution and contrast degree of the ultrasonic images as well as the influence of intrinsic speckle noises. In this paper, we propose a multisemantic attention networks (MSAN) for finegrained thyroid nodule classification in ultrasound images. Specifically, we employ a main network branch for coarse granularity feature extraction, which only focuses on the benign and malignant characteristics, and simultaneously employ multisemantic network branches to extract discriminative features from the finegrained pathological categories. Meanwhile, we introduce an selfattention scheme together with global average pooling (GAP) in our network, which facilitates to learn from the dynamicallyselected nodule regions ranging from local to global. Extensive experiments demonstrate that, our MSAN gives rise to significant improvement of classification accuracy and outperforms the stateoftheart methods.