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A Swin Transformer-Based Model for Thyroid Nodule Detection in Ultrasound Images
Published on: April 21, 2023
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Corrigendum to: Super-resolution based Nodule Localization in Thyroid Ultrasound Images through Deep Learning
Jing Li1, Qiang Guo1, Shiyi Peng1
1Department of Ultrasound, Qingpu Branch of Zhongshan Hospital Affiliated to Fudan University, No. 1158 Gongyuan East Road, Qingpu District, Shanghai, 201700, China.
Current Medical Imaging
|August 26, 2024
Summary
This study introduces a deep learning model for precise thyroid nodule localization in ultrasound images, enhancing diagnostic accuracy. The research is supported by funding from Zhongshan Hospital and the Qingpu District Health Commission.
Area of Science:
- Medical Imaging
- Artificial Intelligence
- Oncology
Background:
- Thyroid nodules are common, and accurate localization in ultrasound images is crucial for diagnosis.
- Deep learning offers potential for improving the accuracy and efficiency of nodule detection.
Purpose of the Study:
- To develop and validate a superresolution-based deep learning model for localizing thyroid nodules in ultrasound images.
- To enhance the precision of nodule identification compared to conventional methods.
Main Methods:
- A deep learning framework incorporating superresolution techniques was employed.
- The model was trained and evaluated on a dataset of thyroid ultrasound images.
Main Results:
- The superresolution-based deep learning model demonstrated high accuracy in localizing thyroid nodules.
- The proposed method showed significant improvements in localization precision.
Conclusions:
- Deep learning, particularly with superresolution, is a promising approach for accurate thyroid nodule localization in ultrasound.
- This technology can aid clinicians in better diagnosing and managing thyroid nodules.
