  Ultrasound examination is widely used in the clinical diagnosis of thyroid
nodules (benign/malignant). However, the accuracy relies heavily on radiologist
experience. Although deep learning techniques have been investigated for
thyroid nodules recognition. Current solutions are mainly based on static
ultrasound images, with limited temporal information used and inconsistent with
clinical diagnosis. This paper proposes a novel method for the automated
recognition of thyroid nodules through an exhaustive exploration of ultrasound
videos and key-frames. We first propose a detection-localization framework to
automatically identify the clinical key-frame with a typical nodule in each
ultrasound video. Based on the localized key-frame, we develop a key-frame
guided video classification model for thyroid nodule recognition. Besides, we
introduce a motion attention module to help the network focus on significant
frames in an ultrasound video, which is consistent with clinical diagnosis. The
proposed thyroid nodule recognition framework is validated on clinically
collected ultrasound videos, demonstrating superior performance compared with
other state-of-the-art methods.
