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Updated: Aug 1, 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

Face-based age estimation using improved Swin Transformer with attention-based convolution.

Chaojun Shi^1,2, Shiwei Zhao¹, Ke Zhang^1,2

¹Department of Electronic and Communication Engineering, North China Electric Power University, Baoding, Hebei, China.

Frontiers in Neuroscience

|May 1, 2023

Summary

This summary is machine-generated.

This study introduces an improved Swin Transformer with attention-based convolution (ABC) for more accurate facial age estimation. The framework effectively extracts age-specific facial features, outperforming existing methods on benchmark datasets.

Keywords:

Swin Transformer age estimation attention mechanism deep learning neural networks

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Area of Science:

Computer Vision
Machine Learning
Deep Learning

Background:

Transformer models, utilizing self-multihead attention, are a novel direction in computer vision.
Convolutional Neural Networks (CNNs) traditionally used, but Transformers capture global context effectively.
Facial patches with age-specific information are crucial for accurate age estimation.

Purpose of the Study:

To propose an attention-based convolution (ABC) framework integrated with Swin Transformer for enhanced facial age estimation.
To leverage the strengths of both shallow convolutional networks with attention and Swin Transformers for feature extraction.
To improve the learning of long-range dependencies and the discovery of diverse, important facial patches.

Main Methods:

Developed an attention-based convolution (ABC) module using a shallow CNN and multiheaded attention to extract age-specific facial patches.
Integrated ABC-extracted features with the Swin Transformer by splicing them with the flattened image.
Introduced a loss of diversity to guide the self-attention mechanism, reducing patch overlap and promoting discovery of unique regions.

Main Results:

The proposed improved Swin Transformer with ABC framework demonstrated superior performance in age estimation.
The integration of ABC effectively utilized the long-dependency modeling of Swin Transformer for stronger feature extraction.
Experiments confirmed the framework's outperformance against several state-of-the-art methods on age estimation benchmark datasets.

Conclusions:

The proposed ABC framework significantly enhances facial age estimation accuracy by effectively integrating crucial facial patches.
The method successfully combines local feature extraction with global context modeling, leading to more robust age prediction.
This approach represents a promising advancement in age estimation technology, outperforming current leading methods.