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The Thyroid Gland

The Thyroid Gland

The thyroid gland is a small, butterfly-shaped gland located in the neck and covers the anterior surface of the trachea. The gland has two lateral lobes connected by a thin tissue mass called the isthmus. Internally, each lobe comprises many small spherical structures known as thyroid follicles, surrounded by a network of blood vessels.
The follicles have a central cavity lined by simple cuboidal to squamous epithelial cells called follicular cells. These cells produce the glycoprotein...

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Related Experiment Video

Updated: Oct 5, 2025

A Swin Transformer-Based Model for Thyroid Nodule Detection in Ultrasound Images

A Swin Transformer-Based Model for Thyroid Nodule Detection in Ultrasound Images

Published on: April 21, 2023

Multi-channel convolutional neural network architectures for thyroid cancer detection.

Xinyu Zhang¹, Vincent C S Lee¹, Jia Rong¹

¹Department of Data Science and AI/Faculty of IT, Monash University, Melbourne, Victoria, Australia.

|January 21, 2022

Summary

This study introduces a deep learning framework using Xception neural networks for early thyroid cancer detection. The AI model achieves high accuracy with medical images, aiming to improve diagnostic efficiency and clinician trust.

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Last Updated: Oct 5, 2025

A Swin Transformer-Based Model for Thyroid Nodule Detection in Ultrasound Images

A Swin Transformer-Based Model for Thyroid Nodule Detection in Ultrasound Images

Published on: April 21, 2023

Author Spotlight: Integrating Ultrasound Imaging with Biochemical Markers for Thyroid Disease Diagnosis

Author Spotlight: Integrating Ultrasound Imaging with Biochemical Markers for Thyroid Disease Diagnosis

Published on: February 9, 2024

Area of Science:

Medical Imaging
Artificial Intelligence
Oncology

Background:

Current thyroid nodule diagnosis relies on medical imaging, facing limitations with human false-positive rates.
Deep learning offers potential for earlier detection and improved accuracy in computer-aided diagnosis (CADx).
Clinician adoption of CADx techniques remains a significant challenge.

Purpose of the Study:

To develop and evaluate a practical deep learning framework for thyroid cancer detection using multi-channel architectures.
To compare the diagnostic performance of ultrasound and computed tomography (CT) scans within the proposed framework.
To enhance clinician trust and adoption of CADx through interpretable results.

Main Methods:

Utilized the Xception neural network as the base structure for a novel CADx framework.
Designed and evaluated three adaptable multi-channel architectures using real-world medical image datasets.
Implemented patient-specific design capabilities for thyroid cancer detection.

Main Results:

The single input dual-channel architecture achieved diagnostic accuracies of 0.989 for ultrasound and 0.975 for CT scans.
Patient-specific designs reached accuracies of 0.95 (double inputs dual-channel) and 0.94 (four-channel architecture).
Ultrasound and CT scans demonstrated comparable diagnostic results, with CT enabling patient-specific designs.

Conclusions:

The proposed deep learning framework significantly outperforms existing methods in thyroid cancer diagnosis.
The framework offers clinicians adaptable architectures and interpretable results, fostering trust and adoption.
This approach promises increased efficiency and accuracy in early thyroid cancer detection and patient-specific treatment planning.