# Anomaly Detection in Medical Imaging with Deep Perceptual Autoencoders

arXiv: https://arxiv.org/abs/2006.13265 · HTML: https://mlsanity.com/paper/2006.13265/

## Key facts

- Cited by 30 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 23 Jun 2020.

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

- [Self-Supervised Masking for Unsupervised Anomaly Detection and Localization](https://mlsanity.com/paper/2205.06568/) (2022)
- [Anomaly Detection under Distribution Shift](https://mlsanity.com/paper/2303.13845/) (2023)
- [Self-Supervised Masked Convolutional Transformer Block for Anomaly Detection](https://mlsanity.com/paper/2209.12148/) (2022)
- [MedIAnomaly: A comparative study of anomaly detection in medical images](https://mlsanity.com/paper/2404.04518/) (2024)
- [Medical Image Captioning via Generative Pretrained Transformers](https://mlsanity.com/paper/2209.13983/) (2022)
- [Deep Learning Advancements in Anomaly Detection: A Comprehensive Survey](https://mlsanity.com/paper/2503.13195/) (2025)
- [Task-oriented Self-supervised Learning for Anomaly Detection in Electroencephalography](https://mlsanity.com/paper/2207.01391/) (2022)
- [Learning image representations for anomaly detection: application to discovery of histological alterations in drug development](https://mlsanity.com/paper/2210.07675/) (2022)
- [Fair Anomaly Detection For Imbalanced Groups](https://mlsanity.com/paper/2409.10951/) (2024)
- [Masked Autoencoders for Unsupervised Anomaly Detection in Medical Images](https://mlsanity.com/paper/2307.07534/) (2023)
- [AI-based Anomaly Detection for Clinical-Grade Histopathological Diagnostics](https://mlsanity.com/paper/2406.14866/) (2024)
- [Scale-Aware Contrastive Reverse Distillation for Unsupervised Medical Anomaly Detection](https://mlsanity.com/paper/2503.13828/) (2025)
- [Landmarks Augmentation with Manifold-Barycentric Oversampling](https://mlsanity.com/paper/2104.00925/) (2021)
- [Hierarchical Visual Categories Modeling: A Joint Representation Learning and Density Estimation Framework for Out-of-Distribution Detection](https://mlsanity.com/paper/2408.15580/) (2024)
- [AD-NEV: A Scalable Multi-level Neuroevolution Framework for Multivariate Anomaly Detection](https://mlsanity.com/paper/2305.16497/) (2023)
- [Feasibility of Universal Anomaly Detection without Knowing the Abnormality in Medical Images](https://mlsanity.com/paper/2307.00750/) (2023)
- [Dual Distillation for Few-Shot Anomaly Detection](https://mlsanity.com/paper/2603.01713/) (2026)
- [TagOOD: A Novel Approach to Out-of-Distribution Detection via Vision-Language Representations and Class Center Learning](https://mlsanity.com/paper/2408.15566/) (2024)
- [MEATRD: Multimodal Anomalous Tissue Region Detection Enhanced with Spatial Transcriptomics](https://mlsanity.com/paper/2412.10659/) (2024)
- [Q-Former Autoencoder: A Modern Framework for Medical Anomaly Detection](https://mlsanity.com/paper/2507.18481/) (2025)
- [Integration of deep generative Anomaly Detection algorithm in high-speed industrial line](https://mlsanity.com/paper/2603.07577/) (2026)
- [Quantitative Benchmarking of Anomaly Detection Methods in Digital Pathology](https://mlsanity.com/paper/2506.19234/) (2025)
- [INFORM-CT: INtegrating LLMs and VLMs FOR Incidental Findings Management in Abdominal CT](https://mlsanity.com/paper/2512.14732/) (2025)
- [Multi-AD: Cross-Domain Unsupervised Anomaly Detection for Medical and Industrial Applications](https://mlsanity.com/paper/2602.05426/) (2026)
- [AD-NEv++ : The multi-architecture neuroevolution-based multivariate anomaly detection framework](https://mlsanity.com/paper/2404.07968/) (2024)
- [U2AD: Uncertainty-based Unsupervised Anomaly Detection Framework for Detecting T2 Hyperintensity in MRI Spinal Cord](https://mlsanity.com/paper/2503.13400/) (2025)
- [Towards Modality-Agnostic Medical Image Anomaly Detection: A Training-Free Manifold Refinement Approach](https://mlsanity.com/paper/2604.19191/) (2026)
- [Anatomy-Aware Unsupervised Detection and Localization of Retinal Abnormalities in Optical Coherence Tomography](https://mlsanity.com/paper/2604.22139/) (2026)
- [AUCp: Pseudo-AUC for Inference Model Selection with Unlabeled Validation Data in Abnormality Detection](https://mlsanity.com/paper/2606.08742/) (2026)
- [Quasi-Binarized Autoencoders: An Architecture-Independent Information Bottleneck for Medical Image Anomaly Detection](https://mlsanity.com/paper/2610.09670/) (2026)

## Abstract (arXiv)

Anomaly detection is the problem of recognizing abnormal inputs based on the seen examples of normal data. Despite recent advances of deep learning in recognizing image anomalies, these methods still prove incapable of handling complex medical images, such as barely visible abnormalities in chest X-rays and metastases in lymph nodes. To address this problem, we introduce a new powerful method of image anomaly detection. It relies on the classical autoencoder approach with a re-designed training pipeline to handle high-resolution, complex images and a robust way of computing an image abnormality score. We revisit the very problem statement of fully unsupervised anomaly detection, where no abnormal examples at all are provided during the model setup. We propose to relax this unrealistic assumption by using a very small number of anomalies of confined variability merely to initiate the search of hyperparameters of the model. We evaluate our solution on natural image datasets with a known benchmark, as well as on two medical datasets containing radiology and digital pathology images. The proposed approach suggests a new strong baseline for image anomaly detection and outperforms state-of-the-art approaches in complex medical image analysis tasks.