Automatic classification of thyroid nodules in ultrasound images using a multi-task attention network guided by clinical knowledge. (November 2022) - Deng, Pengju; Han, Xiaohong; Wei, Xi; Chang, Luchen
Abstract: Thyroid cancer has been the most prevalent cancer in the recent three decades. Ultrasonography is one of the mainly used methods for diagnosing thyroid nodules. Several computer-aided diagnostic methods were proposed to aid radiologists in analyzing ultrasound images of the thyroid gland. Most methods, however, only determine the benignity or malignancy of the thyroid nodule and do not explain the decision-making process of them, which cannot gain the trustworthiness of clinicians because they are not consistent with the physician's diagnostic process. In our work, we design a multi-task branching attention network in which each of the descriptors of the ACR TI-RADS lexicon is first classified. All respective scores are calculated to get the risk stratification of the nodule. Ultimately, based on the risk stratification, the benignity or malignancy of the nodule is determined. This work provides an automated method that incorporates the ACR TI-RADS characterization of thyroid nodules for detecting the level of risk and the benignity or malignancy of thyroid nodules. Thus the work establishes the trustworthiness of clinicians in deep learning models and improves physician efficiency and diagnostic rates to some extent compared to previous studies. For the diagnosis of thyroid nodules, evaluation indices including accuracy, sensitivity, and specificity were 93.55%, 93.86%, and 93.14%, respectively. The experiments show that our approach obtains comparable performance to most advanced methods in diagnosing ultrasound images of the thyroid nodules and is supported by explanations in clinical terms using the ACR TI-RADS lexicon. Highlights: A thyroid cancer diagnosis model is developed. A multitasking attention network guided by clinical knowledge is proposed. The method is consistent with clinicians' visual perception and inference process. Our method achieves comparable performance with state-of-the-art methods.