Description

Thyroid nodule is one of the most common endocrine diseases in the adult population. Early screening and diagnosis of thyroid nodules is of great significance to patients’ subsequent treatment. An objective and accurate diagnosis algorithm for thyroid nodules is vital to improve the efficiency of clinical diagnosis and reduce the work pressure of doctors. In this paper, we propose a fused deep learning model for the diagnosis of benign and malignant thyroid nodules. Based on the ACR TI-RADS proposed by the American Academy of Radiology, the model first extracts 33 clinically significant statistical features of composition, echogenicity, shape, margin, and echogenic foci.

After PCA dimensionality reduction, the top 4 features are fused with the feature map of Efficient Net B3. The thyroid nodules are classified into benign and malignant based on the fused feature map. This model is trained and tested on 3828 clinically collected ultrasound images of thyroid nodules. It achieves an accuracy of 96.4% and an AUC of 0.965. Compared with traditional machine learning algorithms and state-of-the-art deep learning networks, the fused model we proposed has improved classification performance and amount of parameters, and is also clinically interpretable. Thyroid nodules are lesions requiring diagnosis and follow-up. Tools for detecting and segmenting nodules can help physicians with this diagnosis. Besides immediate diagnosis, automated tools can also enable tracking of the probability of malignancy over time. This paper demonstrates a new algorithm for segmenting thyroid nodules in ultrasound images. The algorithm combines traditional supervised semantic segmentation with unsupervised learning using GANs. The hybrid approach has the potential to upgrade the semantic segmentation model’s performance, but GANs have the well-known problems of unstable learning and mode collapse. To stabilize the training of the GAN model, we introduce the concept of closed-loop control of the gain on the loss output of the discriminator. We find gain control leads to smoother generator training and avoids the mode collapse that typically occurs when the discriminator learns too quickly relative to the generator. We also find that the combination of the supervised and unsupervised learning styles encourages both low-level accuracy and high-level consistency.

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With Regards
Jennie

Journal Coordinator
Journal of Reproductive Endocrinology & Infertility