Cascade UNet and CH-UNet for Thyroid Nodule Segmentation and Benign and Malignant Classification

Abstract

The thyroid gland secretes indispensable hormones that are necessary for all the cells in your body to work normally. In order to diagnose and treat thyroid cancer at the earliest stage, it is desired to characterize the nodule accurately. We proposed cascade UNet and CH-UNet to segment thyroid nodules and classify benign and malignant thyroid nodules, respectively. Cascade UNet consists of UNet-I and UNet-II, which segment the nodules in the image at uniform resolution and original resolution, respectively. CH-UNet takes segmentation as an auxiliary task to improve classification performance. We verified our method on the test set of the TNSCUI 2020 Challenge. We achieved 81.73% IoU on segmentation and 0.8551 F1 score on classification, which won the first place in the classification track and was only 0.81% IoU away from the first place in the segmentation track.