Unsupervised Anomaly Localization with Structural Feature-Autoencoders
This work addresses anomaly detection in medical imaging for healthcare applications, representing an incremental improvement over existing methods.
The paper tackles the problem of imperfect anomaly localization in medical images due to large residuals from complex anatomical structures and intensity differences, by proposing a feature-mapping function and Autoencoder with structural similarity loss, resulting in significantly increased performance on two brain MRI datasets.
Unsupervised Anomaly Detection has become a popular method to detect pathologies in medical images as it does not require supervision or labels for training. Most commonly, the anomaly detection model generates a "normal" version of an input image, and the pixel-wise $l^p$-difference of the two is used to localize anomalies. However, large residuals often occur due to imperfect reconstruction of the complex anatomical structures present in most medical images. This method also fails to detect anomalies that are not characterized by large intensity differences to the surrounding tissue. We propose to tackle this problem using a feature-mapping function that transforms the input intensity images into a space with multiple channels where anomalies can be detected along different discriminative feature maps extracted from the original image. We then train an Autoencoder model in this space using structural similarity loss that does not only consider differences in intensity but also in contrast and structure. Our method significantly increases performance on two medical data sets for brain MRI. Code and experiments are available at https://github.com/FeliMe/feature-autoencoder