Anomaly Detection in Medical Imaging with Deep Perceptual Autoencoders

TL;DR

This paper introduces a new deep autoencoder-based method for anomaly detection in complex medical images, improving detection accuracy by handling high-resolution data and minimal anomaly examples.

Contribution

It presents a novel training pipeline and scoring method for autoencoders, relaxing the need for extensive abnormal data and setting a new baseline for medical image anomaly detection.

Findings

Outperforms state-of-the-art methods on medical image datasets

Effective with minimal abnormal examples for hyperparameter tuning

Handles high-resolution, complex medical images successfully

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.