# Anomaly Detection in Medical Imaging -- A Mini Review

arXiv: https://arxiv.org/abs/2108.11986 · HTML: https://mlsanity.com/paper/2108.11986/

## Key facts

- Cited by 44 papers in the map (data through 14 Oct 2026).
- Builds on 16 of its 54 references that are in the map.
- First posted on arXiv 25 Aug 2021.

## Builds on (most cited first, 16 of 16)

- [U-Net: Convolutional Networks for Biomedical Image Segmentation](https://mlsanity.com/paper/1505.04597/) (2015)
- [Unsupervised Anomaly Detection with Generative Adversarial Networks to Guide Marker Discovery](https://mlsanity.com/paper/1703.05921/) (2017)
- [Memorizing Normality to Detect Anomaly: Memory-augmented Deep Autoencoder for Unsupervised Anomaly Detection](https://mlsanity.com/paper/1904.02639/) (2019)
- [Autoencoders for Unsupervised Anomaly Segmentation in Brain MR Images: A Comparative Study](https://mlsanity.com/paper/2004.03271/) (2020)
- [Deep Autoencoding Models for Unsupervised Anomaly Segmentation in Brain MR Images](https://mlsanity.com/paper/1804.04488/) (2018)
- [Unsupervised Anomaly Localization using Variational Auto-Encoders](https://mlsanity.com/paper/1907.02796/) (2019)
- [Unsupervised Lesion Detection via Image Restoration with a Normative Prior](https://mlsanity.com/paper/2005.00031/) (2020)
- [MADGAN: unsupervised Medical Anomaly Detection GAN using multiple adjacent brain MRI slice reconstruction](https://mlsanity.com/paper/2007.13559/) (2020)
- [Unsupervised Medical Image Translation Using Cycle-MedGAN](https://mlsanity.com/paper/1903.03374/) (2019)
- [Exploiting Epistemic Uncertainty of Anatomy Segmentation for Anomaly Detection in Retinal OCT](https://mlsanity.com/paper/1905.12806/) (2019)
- [Unsupervised brain lesion segmentation from MRI using a convolutional autoencoder](https://mlsanity.com/paper/1811.09655/) (2018)
- [DeScarGAN: Disease-Specific Anomaly Detection with Weak Supervision](https://mlsanity.com/paper/2007.14118/) (2020)
- [Distributional Gaussian Process Layers for Outlier Detection in Image Segmentation](https://mlsanity.com/paper/2104.13756/) (2021)
- [PCA leverage: outlier detection for high-dimensional functional magnetic resonance imaging data](https://mlsanity.com/paper/1509.00882/) (2015)
- [Image Synthesis as a Pretext for Unsupervised Histopathological Diagnosis](https://mlsanity.com/paper/2104.13797/) (2021)
- [Detecting abnormalities in resting-state dynamics: An unsupervised learning approach](https://mlsanity.com/paper/1908.06168/) (2019)

## Built on by (most cited first, 30 of 44)

- [Diffusion Models for Medical Image Analysis: A Comprehensive Survey](https://mlsanity.com/paper/2211.07804/) (2022)
- [Transformers in Medical Imaging: A Survey](https://mlsanity.com/paper/2201.09873/) (2022)
- [Deep Learning for Unsupervised Anomaly Localization in Industrial Images: A Survey](https://mlsanity.com/paper/2207.10298/) (2022)
- [CLIP in Medical Imaging: A Survey](https://mlsanity.com/paper/2312.07353/) (2023)
- [Out-of-distribution Detection in Medical Image Analysis: A survey](https://mlsanity.com/paper/2404.18279/) (2024)
- [Fr'echet Radiomic Distance (FRD): A Versatile Metric for Comparing Medical Imaging Datasets](https://mlsanity.com/paper/2412.01496/) (2024)
- [A Coarse-to-Fine Pseudo-Labeling (C2FPL) Framework for Unsupervised Video Anomaly Detection](https://mlsanity.com/paper/2310.17650/) (2023)
- [Exploiting Structural Consistency of Chest Anatomy for Unsupervised Anomaly Detection in Radiography Images](https://mlsanity.com/paper/2403.08689/) (2024)
- [PatchGuard: Adversarially Robust Anomaly Detection and Localization through Vision Transformers and Pseudo Anomalies](https://mlsanity.com/paper/2506.09237/) (2025)
- [Applications of Large Models in Medicine](https://mlsanity.com/paper/2502.17132/) (2025)
- [Is Hyperbolic Space All You Need for Medical Anomaly Detection?](https://mlsanity.com/paper/2505.21228/) (2025)
- [Confidence-Aware and Self-Supervised Image Anomaly Localisation](https://mlsanity.com/paper/2303.13227/) (2023)
- [ISSTAD: Incremental Self-Supervised Learning Based on Transformer for Anomaly Detection and Localization](https://mlsanity.com/paper/2303.17354/) (2023)
- [A Two-Stage Generative Model with CycleGAN and Joint Diffusion for MRI-based Brain Tumor Detection](https://mlsanity.com/paper/2311.03074/) (2023)
- [NERO: Explainable Out-of-Distribution Detection with Neuron-level Relevance](https://mlsanity.com/paper/2506.15404/) (2025)
- [DinoAtten3D: Slice-Level Attention Aggregation of DinoV2 for 3D Brain MRI Anomaly Classification](https://mlsanity.com/paper/2509.12512/) (2025)
- [Identification of Rare Cortical Folding Patterns using Unsupervised Deep Learning](https://mlsanity.com/paper/2211.16213/) (2022)
- [Targeted collapse regularized autoencoder for anomaly detection: black hole at the center](https://mlsanity.com/paper/2306.12627/) (2023)
- [MCDDPM: Multichannel Conditional Denoising Diffusion Model for Unsupervised Anomaly Detection in Brain MRI](https://mlsanity.com/paper/2409.19623/) (2024)
- [Exploring Zero-Shot Anomaly Detection with CLIP in Medical Imaging: Are We There Yet?](https://mlsanity.com/paper/2411.09310/) (2024)
- [MEATRD: Multimodal Anomalous Tissue Region Detection Enhanced with Spatial Transcriptomics](https://mlsanity.com/paper/2412.10659/) (2024)
- ['A net for everyone': fully personalized and unsupervised neural networks trained with longitudinal data from a single patient](https://mlsanity.com/paper/2210.14228/) (2022)
- [LEAN-DMKDE: Quantum Latent Density Estimation for Anomaly Detection](https://mlsanity.com/paper/2211.08525/) (2022)
- [Multiple Instance Ensembling For Paranasal Anomaly Classification In The Maxillary Sinus](https://mlsanity.com/paper/2303.17915/) (2023)
- [AnoOnly: Semi-Supervised Anomaly Detection with the Only Loss on Anomalies](https://mlsanity.com/paper/2305.18798/) (2023)
- [Topological Data Analysis in smart manufacturing: State of the art and futuredirections](https://mlsanity.com/paper/2310.09319/) (2023)
- [Introducing 'Inside' Out of Distribution](https://mlsanity.com/paper/2407.04534/) (2024)
- [Competency-Aware Planning for Probabilistically Safe Navigation Under Perception Uncertainty](https://mlsanity.com/paper/2409.06111/) (2024)
- [PaRCE: Probabilistic and Reconstruction-based Competency Estimation for CNN-based Image Classification](https://mlsanity.com/paper/2411.16715/) (2024)
- [Unsupervised Deep Generative Models for Anomaly Detection in Neuroimaging: A Systematic Scoping Review](https://mlsanity.com/paper/2510.14462/) (2025)

## Abstract (arXiv)

The increasing digitization of medical imaging enables machine learning based improvements in detecting, visualizing and segmenting lesions, easing the workload for medical experts. However, supervised machine learning requires reliable labelled data, which is is often difficult or impossible to collect or at least time consuming and thereby costly. Therefore methods requiring only partly labeled data (semi-supervised) or no labeling at all (unsupervised methods) have been applied more regularly. Anomaly detection is one possible methodology that is able to leverage semi-supervised and unsupervised methods to handle medical imaging tasks like classification and segmentation. This paper uses a semi-exhaustive literature review of relevant anomaly detection papers in medical imaging to cluster into applications, highlight important results, establish lessons learned and give further advice on how to approach anomaly detection in medical imaging. The qualitative analysis is based on google scholar and 4 different search terms, resulting in 120 different analysed papers. The main results showed that the current research is mostly motivated by reducing the need for labelled data. Also, the successful and substantial amount of research in the brain MRI domain shows the potential for applications in further domains like OCT and chest X-ray.