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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

Classification regularized dimensionality reduction improves ultrasound thyroid nodule diagnostic accuracy and

Wenli Dai¹, Yan Cui¹, Peiyi Wang¹

¹School of Mathematical Sciences, Zhejiang University, Hangzhou, China.

Computers in Biology and Medicine

|January 28, 2023

Summary

A new method, Classification Regularized Uniform Manifold Approximation and Projection (CReUMAP), enhances medical image analysis by combining Convolutional Neural Networks and dimensionality reduction. This approach improves diagnostic accuracy and confidence for radiologists interpreting thyroid nodules.

Keywords:

Deep learning Dimensionality reduction High dimensional feature interpretability Inter-observer variability Medical image-based diagnosis Thyroid nodule

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Area of Science:

Medical Imaging
Machine Learning
Radiology

Background:

Convolutional Neural Networks (CNNs) in medical imaging often provide only probability outputs, lacking detailed image insights.
Dimensionality Reduction Techniques (DRTs) are primarily for visualization, not discriminative classification.

Purpose of the Study:

To develop an interactive visualization system for medical images that reflects pathological characteristics and lesion similarity.
To assist radiologists in diagnosis and medical research by enhancing understanding of image data.

Main Methods:

Proposed Classification Regularized Uniform Manifold Approximation and Projection (CReUMAP), integrating CNN feature vectors with malignancy probabilities.
Projected fused data into a 2D space for spatial segmentation classification.
Applied CReUMAP to classify thyroid nodule malignancy using 2614 ultrasound images.

Main Results:

CReUMAP embedding showed strong correlation with TI-RADS categories for thyroid nodules.
Significantly improved diagnostic accuracy (p=0.016) and confidence (p=1.902×10⁻⁶) for radiologists.
Achieved 90.8% accuracy, 92.1% sensitivity, and 88.6% specificity on a test set of 303 images.

Conclusions:

CReUMAP embedding aligns well with pathological diagnosis, leading to more accurate, confident, and consistent radiologist diagnoses.
Enables medical centers to create locally adapted, updateable embeddings from trained models.