Identifying Thyroid Nodules in Ultrasound Images Through Segmentation-Guided Discriminative Localization

In this paper, we propose a novel segmentation-guided network for thyroid nodule identification from ultrasound images. Accurate diagnosis of thyroid nodules through ultrasound images is significant for cancer detection at the early stage. Many Computer-Aided Diagnose (CAD) systems for this task ignore the inherent correlation between nodule segmentation task and classification task (i.e. cancer grading). Actually, segmentation results could be used as localization cues of thyroid nodules for facilitating their classifications as benign or malignant. Accordingly, we propose a two-stage thyroid nodule diagnosis method through 1) nodule segmentation and 2) segmentation-guided diagnosis. Specifically, in the segmentation stage, we use an ensemble strategy to integrate segmentations from diverse segmentation networks. Then, in the classification stage, the obtained segmentation result is integrated as additional information along with its corresponding original ultrasound images as the input of the classification network. Meanwhile, the segmentation result is further served as guidance to refine the attention map of the features used for classification. Our method is applied to the TN-SCUI 2020, a MICCAI 2020 Challenge, with the largest set of thyroid nodule ultrasound images according to our knowledge. Our method achieved the 2nd place in its classification challenge.