During thyroid ultrasound diagnosis, radiologists add markers such as pluses or crosses near a nodule's edge to indicate the location of a nodule. For computer-aided detection, deep learning models achieve classification, segmentation, and detection by learning the thyroid's texture in ultrasound images. Experiments show that manual markers are strong prior knowledge for data-driven deep learning models, which interferes with the judgment mechanism of computer-aided detection systems. Aiming at this problem, this paper proposes cascade marker removal algorithm for thyroid ultrasound images to eliminate the interference of manual markers. The algorithm consists of three parts. First, in order to highlight marked features, the algorithm extracts salient features in thyroid ultrasound images through feature extraction module. Secondly, mask correction module eliminates the interference of other features besides markers' features. Finally, the marker removal module removes markers without destroying the semantic information in thyroid ultrasound images. Experiments show that our algorithm enables classification, segmentation, and object detection models to focus on the learning of pathological tissue features. At the same time, compared with mainstream image inpainting algorithms, our algorithm shows better performance on thyroid ultrasound images. In summary, our algorithm is of great significance for improving the stability and performance of computer-aided detection systems. Graphical Abstract During thyroid ultrasound diagnosis, doctors add markers such as pluses or crosses near nodule's edge to indicate the location of nodule. Manual markers are strong prior knowledge for data-driven deep learning models, which interferes the judgment mechanism of computer-aided diagnosis system based on deep learning. Markers make models overfit the specific labeling forms easily, and performs poorly on unmarked thyroid ultrasound images. Aiming at this problem, this paper proposes a cascade marker removal algorithm to eliminate the interference of manual markers. Our algorithm make deep learning models pay attention on nodules' features of thyroid ultrasound images, which make computer-aided diagnosis system performs good in both marked imaging and unmarked imaging.
Keywords: Computer-assisted; Deep learning; Diagnosis; Diagnostic imaging; Thyroid neoplasms; Ultrasonography.