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Transformers in Distribution System

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Updated: Oct 8, 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

UTRAD: Anomaly detection and localization with U-Transformer.

Liyang Chen¹, Zhiyuan You¹, Nian Zhang²

¹School of Mechanical Engineering, Shanghai Jiao Tong University, Shanghai 200240, China.

Neural Networks : the Official Journal of the International Neural Network Society

|January 1, 2022

Summary

This summary is machine-generated.

This study introduces UTRAD, a Transformer-based Anomaly Detection framework. UTRAD enhances anomaly detection stability and precision by reconstructing informative feature distributions, outperforming existing methods on diverse datasets.

Keywords:

Anomaly detection Image transformer One-class learning

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Area of Science:

Computer Vision
Machine Learning
Artificial Intelligence

Background:

Anomaly detection is crucial for industrial defect and medical disease identification.
Existing methods face challenges with training stability and universal evaluation criteria for feature distributions.

Purpose of the Study:

To introduce UTRAD, a novel U-Transformer based Anomaly Detection framework.
To improve the stability, precision, and multi-scale detection capabilities of anomaly detection systems.

Main Methods:

Representing deep pre-trained features as 'word tokens' processed by transformer-based autoencoders.
Utilizing reconstruction on informative feature distributions rather than raw images for enhanced detection.
Implementing a multi-scale pyramidal hierarchy with skip connections for comprehensive anomaly detection.

Main Results:

Achieved a more stable training process and precise anomaly detection and localization.
Successfully detected both multi-scale structural and non-structural anomalies.
Demonstrated superior performance compared to state-of-the-art methods on MVtec AD, Retinal-OCT, Brain-MRI, and Head-CT datasets.

Conclusions:

UTRAD offers a robust and efficient framework for anomaly detection across various domains.
The method's multi-scale architecture and feature distribution reconstruction contribute to its high performance.
UTRAD represents a significant advancement in anomaly detection technology, validated across industrial and medical applications.