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
Our strongest model so far 💪. Anomaly detection with image-reconstruction models doesn’t work well if anomalies are not hyperintense. FAE solves this problem.
Fig. 1. FAE trains an autoencoder not in image-space, but in the feature-space of a pretrained ResNet. This allows it to also capture anomalies that are not hyperintense in image-space. Using the Structural Similarity Index Measure (SSIM) further helps with this problem.
Our model outperforms all competitors by a large margin. A recent analysis of 2023 still found it the strongest anomaly detection model so far.
Fig. 2. Performance of all compared models and a random classifier, including error bars that indicate one standard deviation over
Fig. 3. Examples of successful and failure cases.