Dual-branch network via pseudo-label training for thyroid nodule detection in ultrasound image

Song, Ruoning; Zhu, Chuang; Zhang, Long; Zhang, Tong; Luo, Yihao; Li, Jun; Yang, Jie

doi:10.1007/s10489-021-02967-2

Cited by 16 publications

(2 citation statements)

References 61 publications

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“…In Santillan et al (2021) and Song et al (2022) the focus is on the detection of malignant thyroid nodules which could possibly generate cancer. The former apply convolutional and recurrent neural networks to Fourier Transform infrared spectroscopy data achieving accuracy of 98.06%.…”

Section: Resultsmentioning

confidence: 99%

A systematic review on artificial intelligence techniques for detecting thyroid diseases

Aversano,

Bernardi,

Cimitile

et al. 2023

PeerJ Computer Science

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The use of artificial intelligence approaches in health-care systems has grown rapidly over the last few years. In this context, early detection of diseases is the most common area of application. In this scenario, thyroid diseases are an example of illnesses that can be effectively faced if discovered quite early. Detecting thyroid diseases is crucial in order to treat patients effectively and promptly, by saving lives and reducing healthcare costs. This work aims at systematically reviewing and analyzing the literature on various artificial intelligence-related techniques applied to the detection and identification of various diseases related to the thyroid gland. The contributions we reviewed are classified according to different viewpoints and taxonomies in order to highlight pros and cons of the most recent research in the field. After a careful selection process, we selected and reviewed 72 papers, analyzing them according to three main research questions, i.e., which diseases of the thyroid gland are detected by different artificial intelligence techniques, which datasets are used to perform the aforementioned detection, and what types of data are used to perform the detection. The review demonstrates that the majority of the considered papers deal with supervised methods to detect hypo- and hyperthyroidism. The average accuracy of detection is high (96.84%), but the usage of private and outdated datasets with a majority of clinical data is very common. Finally, we discuss the outcomes of the systematic review, pointing out advantages, disadvantages, and future developments in the application of artificial intelligence for thyroid diseases detection.

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Section: Resultsmentioning

confidence: 99%

A systematic review on artificial intelligence techniques for detecting thyroid diseases

Aversano,

Bernardi,

Cimitile

et al. 2023

PeerJ Computer Science

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show abstract

“…However, due to the poor quality of medical ultrasound imaging and the lack of obvious image differences between the nodules and the surrounding tissue 20 , it is not conducive to the network extracting more effective features, and the classification performance is impaired. Therefore, it is necessary to improve the feature extraction ability of the network.…”

Section: Introductionmentioning

confidence: 99%

A novel ultrasound image diagnostic method for thyroid nodules

Zheng

Wang

et al. 2023

Sci Rep

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The incidence of thyroid nodules is increasing year by year. Accurate determination of benign and malignant nodules is an important basis for formulating treatment plans. Ultrasonography is the most widely used methodology in the diagnosis of benign and malignant nodules, but diagnosis by doctors is highly subjective, and the rates of missed diagnosis and misdiagnosis are high. To improve the accuracy of clinical diagnosis, this paper proposes a new diagnostic model based on deep learning. The diagnostic model adopts the diagnostic strategy of localization-classification. First, the distribution laws of the nodule size and nodule aspect ratio are obtained through data statistics, a multiscale localization network structure is a priori designed, and the nodule aspect ratio is obtained from the positioning results. Then, uncropped ultrasound images and nodule area image are correspondingly input into a two-way classification network, and an improved attention mechanism is used to enhance the feature extraction performance. Finally, the deep features, the shallow features, and the nodule aspect ratio are fused, and a fully connected layer is used to complete the classification of benign and malignant nodules. The experimental dataset consists of 4021 ultrasound images, where each image has been labeled under the guidance of doctors, and the ratio of the training set, validation set, and test set sizes is close to 3:1:1. The experimental results show that the accuracy of the multiscale localization network reaches 93.74%, and that the accuracy, specificity, and sensitivity of the classification network reach 86.34%, 81.29%, and 90.48%, respectively. Compared with the champion model of the TNSCUI 2020 classification competition, the accuracy rate is 1.52 points higher. Therefore, the network model proposed in this paper can effectively diagnose benign and malignant thyroid nodules.

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Multi-task Class Feature Space Fusion Domain Adaptation Network for Thyroid Ultrasound Images: Research on Generalization of Smart Healthcare Systems

Xiang

Liu

Gao

et al. 2022

Wireless Algorithms, Systems, and Applications

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Dual-branch network via pseudo-label training for thyroid nodule detection in ultrasound image

Cited by 16 publications

References 61 publications

A systematic review on artificial intelligence techniques for detecting thyroid diseases

A systematic review on artificial intelligence techniques for detecting thyroid diseases

A novel ultrasound image diagnostic method for thyroid nodules

Multi-task Class Feature Space Fusion Domain Adaptation Network for Thyroid Ultrasound Images: Research on Generalization of Smart Healthcare Systems

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