Abstract
Anomaly detection methods generally target the learning of a normal image
distribution (i.e., inliers showing healthy cases) and during testing, samples
relatively far from the learned distribution are classified as anomalies (i.e.,
outliers showing disease cases). These approaches tend to be sensitive to
outliers that lie relatively close to inliers (e.g., a colonoscopy image with a
small polyp). In this paper, we address the inappropriate sensitivity to
outliers by also learning from inliers. We propose a new few-shot anomaly
detection method based on an encoder trained to maximise the mutual information
between feature embeddings and normal images, followed by a few-shot score
inference network, trained with a large set of inliers and a substantially
smaller set of outliers. We evaluate our proposed method on the clinical
problem of detecting frames containing polyps from colonoscopy video sequences,
where the training set has 13350 normal images (i.e., without polyps) and less
than 100 abnormal images (i.e., with polyps). The results of our proposed model
on this data set reveal a state-of-the-art detection result, while the
performance based on different number of anomaly samples is relatively stable
after approximately 40 abnormal training images.