# Unsupervised Pathology Detection: A Deep Dive Into the State of the Art

arXiv: https://arxiv.org/abs/2303.00609 · HTML: https://mlsanity.com/paper/2303.00609/

## Key facts

- Cited by 24 papers in the map (data through 14 Oct 2026).
- Builds on 42 of its 57 references that are in the map.
- First posted on arXiv 1 Mar 2023.

## Builds on (most cited first, 30 of 42)

- [A Simple Framework for Contrastive Learning of Visual Representations](https://mlsanity.com/paper/2002.05709/) (2020)
- [Momentum Contrast for Unsupervised Visual Representation Learning](https://mlsanity.com/paper/1911.05722/) (2019)
- [Grad-CAM: Visual Explanations from Deep Networks via Gradient-based Localization](https://mlsanity.com/paper/1610.02391/) (2016)
- [Wide Residual Networks](https://mlsanity.com/paper/1605.07146/) (2016)
- [Variational Inference with Normalizing Flows](https://mlsanity.com/paper/1505.05770/) (2015)
- [CheXpert: A Large Chest Radiograph Dataset with Uncertainty Labels and Expert Comparison](https://mlsanity.com/paper/1901.07031/) (2019)
- [Swin-Unet: Unet-like Pure Transformer for Medical Image Segmentation](https://mlsanity.com/paper/2105.05537/) (2021)
- [PaDiM: a Patch Distribution Modeling Framework for Anomaly Detection and Localization](https://mlsanity.com/paper/2011.08785/) (2020)
- [Unsupervised Anomaly Detection with Generative Adversarial Networks to Guide Marker Discovery](https://mlsanity.com/paper/1703.05921/) (2017)
- [CutPaste: Self-Supervised Learning for Anomaly Detection and Localization](https://mlsanity.com/paper/2104.04015/) (2021)
- [The RSNA-ASNR-MICCAI BraTS 2021 Benchmark on Brain Tumor Segmentation and Radiogenomic Classification](https://mlsanity.com/paper/2107.02314/) (2021)
- [Using Self-Supervised Learning Can Improve Model Robustness and Uncertainty](https://mlsanity.com/paper/1906.12340/) (2019)
- [Anomaly Detection via Reverse Distillation from One-Class Embedding](https://mlsanity.com/paper/2201.10703/) (2022)
- [Uninformed Students: Student-Teacher Anomaly Detection with Discriminative Latent Embeddings](https://mlsanity.com/paper/1911.02357/) (2019)
- [Transfusion: Understanding Transfer Learning for Medical Imaging](https://mlsanity.com/paper/1902.07208/) (2019)
- [CFLOW-AD: Real-Time Unsupervised Anomaly Detection with Localization via Conditional Normalizing Flows](https://mlsanity.com/paper/2107.12571/) (2021)
- [Sub-Image Anomaly Detection with Deep Pyramid Correspondences](https://mlsanity.com/paper/2005.02357/) (2020)
- [Deep Anomaly Detection Using Geometric Transformations](https://mlsanity.com/paper/1805.10917/) (2018)
- [Improving Unsupervised Defect Segmentation by Applying Structural Similarity to Autoencoders](https://mlsanity.com/paper/1807.02011/) (2018)
- [FastFlow: Unsupervised Anomaly Detection and Localization via 2D Normalizing Flows](https://mlsanity.com/paper/2111.07677/) (2021)
- [Patch SVDD: Patch-level SVDD for Anomaly Detection and Segmentation](https://mlsanity.com/paper/2006.16067/) (2020)
- [PANDA: Adapting Pretrained Features for Anomaly Detection and Segmentation](https://mlsanity.com/paper/2010.05903/) (2020)
- [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)
- [Student-Teacher Feature Pyramid Matching for Anomaly Detection](https://mlsanity.com/paper/2103.04257/) (2021)
- [Modeling the Distribution of Normal Data in Pre-Trained Deep Features for Anomaly Detection](https://mlsanity.com/paper/2005.14140/) (2020)
- [Towards Visually Explaining Variational Autoencoders](https://mlsanity.com/paper/1911.07389/) (2019)
- [Fast Unsupervised Brain Anomaly Detection and Segmentation with Diffusion Models](https://mlsanity.com/paper/2206.03461/) (2022)
- [Unsupervised Detection of Lesions in Brain MRI using constrained adversarial auto-encoders](https://mlsanity.com/paper/1806.04972/) (2018)
- [Unsupervised Anomaly Localization using Variational Auto-Encoders](https://mlsanity.com/paper/1907.02796/) (2019)

## Built on by (most cited first, 24 of 24)

- [MedIAnomaly: A comparative study of anomaly detection in medical images](https://mlsanity.com/paper/2404.04518/) (2024)
- [Guided Reconstruction with Conditioned Diffusion Models for Unsupervised Anomaly Detection in Brain MRIs](https://mlsanity.com/paper/2312.04215/) (2023)
- [Ensembled Cold-Diffusion Restorations for Unsupervised Anomaly Detection](https://mlsanity.com/paper/2407.06635/) (2024)
- [Leveraging the Mahalanobis Distance to enhance Unsupervised Brain MRI Anomaly Detection](https://mlsanity.com/paper/2407.12474/) (2024)
- [DISYRE: Diffusion-Inspired SYnthetic REstoration for Unsupervised Anomaly Detection](https://mlsanity.com/paper/2311.15453/) (2023)
- [A multi-modal vision-language model for generalizable annotation-free pathology localization](https://mlsanity.com/paper/2401.02044/) (2024)
- [Diffusion Models with Ensembled Structure-Based Anomaly Scoring for Unsupervised Anomaly Detection](https://mlsanity.com/paper/2403.14262/) (2024)
- [(Predictable) Performance Bias in Unsupervised Anomaly Detection](https://mlsanity.com/paper/2309.14198/) (2023)
- [Deep Unsupervised Anomaly Detection in Brain Imaging: Large-Scale Benchmarking and Bias Analysis](https://mlsanity.com/paper/2512.01534/) (2025)
- [A Principled Approach to Unsupervised Anomaly Detection](https://mlsanity.com/paper/2609.21800/) (2026)
- [Quantitative Benchmarking of Anomaly Detection Methods in Digital Pathology](https://mlsanity.com/paper/2506.19234/) (2025)
- [OCSVM-Guided Representation Learning for Unsupervised Anomaly Detection](https://mlsanity.com/paper/2507.21164/) (2025)
- [Exploring Scale Shift in Crowd Localization under the Context of Domain Generalization](https://mlsanity.com/paper/2510.19330/) (2025)
- [ART-ASyn: Anatomy-aware Realistic Texture-based Anomaly Synthesis Framework for Chest X-Rays](https://mlsanity.com/paper/2512.00310/) (2025)
- [Towards frugal unsupervised detection of subtle abnormalities in medical imaging](https://mlsanity.com/paper/2309.02458/) (2023)
- [How Low Can You Go? Surfacing Prototypical In-Distribution Samples for Unsupervised Anomaly Detection](https://mlsanity.com/paper/2312.03804/) (2023)
- [Harnessing Intra-group Variations Via a Population-Level Context for Pathology Detection](https://mlsanity.com/paper/2403.02307/) (2024)
- [Semantic Alignment of Unimodal Medical Text and Vision Representations](https://mlsanity.com/paper/2503.04478/) (2025)
- [PathoSCOPE: Few-Shot Pathology Detection via Self-Supervised Contrastive Learning and Pathology-Informed Synthetic Embeddings](https://mlsanity.com/paper/2505.17614/) (2025)
- [Latent Anomaly Detection: Masked VQ-GAN for Unsupervised Segmentation in Medical CBCT](https://mlsanity.com/paper/2506.14209/) (2025)
- [Unsupervised Anomaly Detection in Brain MRI via Disentangled Anatomy Learning](https://mlsanity.com/paper/2512.21924/) (2025)
- [PDD: Manifold-Prior Diverse Distillation for Medical Anomaly Detection](https://mlsanity.com/paper/2603.07142/) (2026)
- [Towards Modality-Agnostic Medical Image Anomaly Detection: A Training-Free Manifold Refinement Approach](https://mlsanity.com/paper/2604.19191/) (2026)
- [MIRTO: a registration-gated, multiverse-tested evaluation protocol for unsupervised anomaly segmentation in brain MRI](https://mlsanity.com/paper/2610.02136/) (2026)

## Abstract (arXiv)

Deep unsupervised approaches are gathering increased attention for applications such as pathology detection and segmentation in medical images since they promise to alleviate the need for large labeled datasets and are more generalizable than their supervised counterparts in detecting any kind of rare pathology. As the Unsupervised Anomaly Detection (UAD) literature continuously grows and new paradigms emerge, it is vital to continuously evaluate and benchmark new methods in a common framework, in order to reassess the state-of-the-art (SOTA) and identify promising research directions. To this end, we evaluate a diverse selection of cutting-edge UAD methods on multiple medical datasets, comparing them against the established SOTA in UAD for brain MRI. Our experiments demonstrate that newly developed feature-modeling methods from the industrial and medical literature achieve increased performance compared to previous work and set the new SOTA in a variety of modalities and datasets. Additionally, we show that such methods are capable of benefiting from recently developed self-supervised pre-training algorithms, further increasing their performance. Finally, we perform a series of experiments in order to gain further insights into some unique characteristics of selected models and datasets. Our code can be found under https://github.com/iolag/UPD_study/.