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Thyroid Nodule

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Application of deep learning as an ancillary diagnostic tool for thyroid FNA cytology.

Cancer cytopathology
BACKGROUND: Several studies have used artificial intelligence (AI) to analyze cytology images, but AI has yet to be adopted in clinical practice. The objective of this study was to demonstrate the accuracy of AI-based image analysis for thyroid fine-...

Investigation of optimal convolutional neural network conditions for thyroid ultrasound image analysis.

Scientific reports
Neural network models have been used to analyze thyroid ultrasound (US) images and stratify malignancy risk of the thyroid nodules. We investigated the optimal neural network condition for thyroid US image analysis. We compared scratch and transfer l...

FDE-net: Frequency-domain enhancement network using dynamic-scale dilated convolution for thyroid nodule segmentation.

Computers in biology and medicine
Thyroid nodules, a common disease of endocrine system, have a probability of nearly 10% to turn into malignant nodules and thus pose a serious threat to health. Automatic segmentation of thyroid nodules is of great importance for clinicopathological ...

A Novel Deep-Learning-Based CADx Architecture for Classification of Thyroid Nodules Using Ultrasound Images.

Interdisciplinary sciences, computational life sciences
Nodules of thyroid cancer occur in the cells of the thyroid as benign or malign types. Thyroid sonographic images are mostly used for diagnosis of thyroid cancer. The aim of this study is to introduce a computer-aided diagnosis system that can classi...

Deep learning for classification of thyroid nodules on ultrasound: validation on an independent dataset.

Clinical imaging
OBJECTIVES: The purpose is to apply a previously validated deep learning algorithm to a new thyroid nodule ultrasound image dataset and compare its performances with radiologists.

Segmentation of thyroid glands and nodules in ultrasound images using the improved U-Net architecture.

BMC medical imaging
BACKGROUND: Identifying thyroid nodules' boundaries is crucial for making an accurate clinical assessment. However, manual segmentation is time-consuming. This paper utilized U-Net and its improved methods to automatically segment thyroid nodules and...

A novel approach to quantify calcifications of thyroid nodules in US images based on deep learning: predicting the risk of cervical lymph node metastasis in papillary thyroid cancer patients.

European radiology
OBJECTIVE: Based on ultrasound (US) images, this study aimed to detect and quantify calcifications of thyroid nodules, which are regarded as one of the most important features in US diagnosis of thyroid cancer, and to further investigate the value of...

The auxiliary diagnosis of thyroid echogenic foci based on a deep learning segmentation model: A two-center study.

European journal of radiology
OBJECTIVE: The aim of this study is to develop AI-assisted software incorporating a deep learning (DL) model based on static ultrasound images. The software aims to aid physicians in distinguishing between malignant and benign thyroid nodules with ec...

Improving the Efficacy of ACR TI-RADS Through Deep Learning-Based Descriptor Augmentation.

Journal of digital imaging
Thyroid nodules occur in up to 68% of people, 95% of which are benign. Of the 5% of malignant nodules, many would not result in symptoms or death, yet 600,000 FNAs are still performed annually, with a PPV of 5-7% (up to 30%). Artificial intelligence ...