Review of unsupervised anomaly detection techniques for medical imaging

Zhao Yingcheng; Song Xiaogang; Shi Zhenghao; You Zhenzhen; Hei Xinhong

doi:10.11959/j.issn.1000-436x.2026046

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Review of unsupervised anomaly detection techniques for medical imaging

Comprehensive Reviews | 更新时间：2026-05-07

- Review of unsupervised anomaly detection techniques for medical imaging
- Journal on Communications Vol. 47, Issue 4, Pages: 248-269(2026)
- 作者机构：
  
  1.西安理工大学计算机科学与工程学院，陕西西安 710048
  2.陕西省网络计算与安全技术重点实验室，陕西西安 710048
  3.人机共融智能机器人陕西省高校工程研究中心，陕西西安 710048
- 作者简介：
- 基金信息：
  
  The National Natural Science Foundation of China(62502377);The Natural Science Basic Research Program of Shaanxi Province(2025JC-YBMS-755;2025JC-YBQN-934)
- DOI：10.11959/j.issn.1000-436x.2026046
  CLC： TP391.41
- Received：01 November 2025，
  
  Revised：2026-02-26，
  
  Accepted：26 February 2026，
  
  Published：20 April 2026
- 稿件说明：
移动端阅览
赵映程,宋霄罡,石争浩等.医学影像无监督异常检测技术综述[J].通信学报,2026,47(04):248-269.

Zhao Yingcheng,Song Xiaogang,Shi Zhenghao,et al.Review of unsupervised anomaly detection techniques for medical imaging[J].Journal on Communications,2026,47(04):248-269.
赵映程,宋霄罡,石争浩等.医学影像无监督异常检测技术综述[J].通信学报,2026,47(04):248-269. DOI： 10.11959/j.issn.1000-436x.2026046.

Zhao Yingcheng,Song Xiaogang,Shi Zhenghao,et al.Review of unsupervised anomaly detection techniques for medical imaging[J].Journal on Communications,2026,47(04):248-269. DOI： 10.11959/j.issn.1000-436x.2026046.

摘要

无监督异常检测技术能够在仅使用正常样本训练的前提下识别偏离分布的异常样本，因此在医学影像领域中能够用于病变的自动检测与区域定位，对辅助疾病筛查，尤其是罕见病的诊断具有重要意义。针对医学影像无监督异常检测领域的研究进展，首先，依据核心检测机制将现有异常检测方法划分为4个主要类别，并详细阐述了每类方法的机理、特性及其代表性工作；其次，汇总了常用的医学影像公开数据集，并归纳了主要的异常评分计算方式以及模型评估基准指标；进而，通过在多模态医学影像数据集上的系统性定量实验，对比了各类代表性方法的检测性能与计算效率，并对其结果进行了深入分析；最后，讨论了该领域在检测性能及临床适用性等方面存在的挑战，并对未来的研究方向进行了展望。

Abstract

Unsupervised anomaly detection technology enabled the identification of out-of-distribution anomalous samples using only normal samples for training. Thus

it could be applied in the field of medical imaging for automated lesion detection and localization

playing a significant role in assisting disease screening

particularly in the diagnosis of rare diseases. Research progress on unsupervised anomaly detection in medical imaging. First

existing anomaly detection methods were categorized into four main types based on their core detection mechanisms

and the principles

characteristics

and representative works of each category were elaborated in detail. Second

commonly used public medical imaging datasets were summarized

and major anomaly scoring methods as well as benchmark evaluation metrics were introduced. Furthermore

through systematic quantitative experiments on multi-modal medical image datasets

the detection performance and computational efficiency of various representative methods were compared

and an in-depth analysis was provided based on the results. Finally

challenges in detection performance and clinical applicability were discussed

and future research directions were outlined.

关键词

Keywords

references

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