# Towards Universal Unsupervised Anomaly Detection in Medical Imaging

arXiv: https://arxiv.org/abs/2401.10637 · HTML: https://mlsanity.com/paper/2401.10637/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- Builds on 16 of its 39 references that are in the map.
- First posted on arXiv 19 Jan 2024.

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

- [The Unreasonable Effectiveness of Deep Features as a Perceptual Metric](https://mlsanity.com/paper/1801.03924/) (2018)
- [Masked Autoencoders Are Scalable Vision Learners](https://mlsanity.com/paper/2111.06377/) (2021)
- [CutPaste: Self-Supervised Learning for Anomaly Detection and Localization](https://mlsanity.com/paper/2104.04015/) (2021)
- [Uninformed Students: Student-Teacher Anomaly Detection with Discriminative Latent Embeddings](https://mlsanity.com/paper/1911.02357/) (2019)
- [PadChest: A large chest x-ray image dataset with multi-label annotated reports](https://mlsanity.com/paper/1901.07441/) (2019)
- [Multiresolution Knowledge Distillation for Anomaly Detection](https://mlsanity.com/paper/2011.11108/) (2020)
- [Standardized Assessment of Automatic Segmentation of White Matter Hyperintensities and Results of the WMH Segmentation Challenge](https://mlsanity.com/paper/1904.00682/) (2019)
- [Unsupervised Anomaly Localization using Variational Auto-Encoders](https://mlsanity.com/paper/1907.02796/) (2019)
- [Context-encoding Variational Autoencoder for Unsupervised Anomaly Detection](https://mlsanity.com/paper/1812.05941/) (2018)
- [Mask, Stitch, and Re-Sample: Enhancing Robustness and Generalizability in Anomaly Detection through Automatic Diffusion Models](https://mlsanity.com/paper/2305.19643/) (2023)
- [Detecting Outliers with Foreign Patch Interpolation](https://mlsanity.com/paper/2011.04197/) (2020)
- [MAEDAY: MAE for few and zero shot AnomalY-Detection](https://mlsanity.com/paper/2211.14307/) (2022)
- [fastMRI+: Clinical Pathology Annotations for Knee and Brain Fully Sampled Multi-Coil MRI Data](https://mlsanity.com/paper/2109.03812/) (2021)
- [Soft-IntroVAE: Analyzing and Improving the Introspective Variational Autoencoder](https://mlsanity.com/paper/2012.13253/) (2020)
- [Anatomy-Guided Weakly-Supervised Abnormality Localization in Chest X-rays](https://mlsanity.com/paper/2206.12704/) (2022)
- [3D Masked Autoencoders with Application to Anomaly Detection in Non-Contrast Enhanced Breast MRI](https://mlsanity.com/paper/2303.05861/) (2023)

## Built on by (most cited first, 6 of 6)

- [Back-in-Time Diffusion: Unsupervised Detection of Medical Deepfakes](https://mlsanity.com/paper/2407.15169/) (2024)
- [Exploring Out-of-distribution Detection for Sparse-view Computed Tomography with Diffusion Models](https://mlsanity.com/paper/2411.06308/) (2024)
- [Deep generative priors for 3D brain analysis](https://mlsanity.com/paper/2510.15119/) (2025)
- [VID-AD: A Dataset for Image-Level Logical Anomaly Detection under Vision-Induced Distraction](https://mlsanity.com/paper/2603.13964/) (2026)
- [Towards Modality-Agnostic Medical Image Anomaly Detection: A Training-Free Manifold Refinement Approach](https://mlsanity.com/paper/2604.19191/) (2026)
- [Unsupervised Anomaly Detection for Image Dataset Quality Assurance in Multi-Center Breast MRI](https://mlsanity.com/paper/2608.16725/) (2026)

## Abstract (arXiv)

The increasing complexity of medical imaging data underscores the need for advanced anomaly detection methods to automatically identify diverse pathologies. Current methods face challenges in capturing the broad spectrum of anomalies, often limiting their use to specific lesion types in brain scans. To address this challenge, we introduce a novel unsupervised approach, termed textitReversed Auto-Encoders (RA), designed to create realistic pseudo-healthy reconstructions that enable the detection of a wider range of pathologies. We evaluate the proposed method across various imaging modalities, including magnetic resonance imaging (MRI) of the brain, pediatric wrist X-ray, and chest X-ray, and demonstrate superior performance in detecting anomalies compared to existing state-of-the-art methods. Our unsupervised anomaly detection approach may enhance diagnostic accuracy in medical imaging by identifying a broader range of unknown pathologies. Our code is publicly available at: urlhttps://github.com/ci-ber/RA.