Anomaly Detection in Medical Imaging with Deep Perceptual Autoencoders

Abstract

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.

arXiv commentThe final authenticated publication is available online at https://ieeexplore.ieee.org/abstract/document/9521238

References in corpus (8)

• ChestX-ray8: Hospital-scale Chest X-ray Database and Benchmarks on Weakly-Supervised Classification and Localization of Common Thorax Diseases
• CheXNet: Radiologist-Level Pneumonia Detection on Chest X-Rays with Deep Learning
• Deep Learning for Anomaly Detection: A Survey
• Deep Learning for Chest X-ray Analysis: A Survey
• Deep Anomaly Detection Using Geometric Transformations
• Deep Structured Energy Based Models for Anomaly Detection
• Unsupervised Detection of Lesions in Brain MRI using constrained adversarial auto-encoders
• Perceptual Image Anomaly Detection

Cited by in corpus (4)

• Self-Supervised Masked Convolutional Transformer Block for Anomaly Detection
• Learning image representations for anomaly detection: application to discovery of histological alterations in drug development
• Multi-AD: Cross-Domain Unsupervised Anomaly Detection for Medical and Industrial Applications
• Landmarks Augmentation with Manifold-Barycentric Oversampling