%0 Journal Article
%T Multi-resolution auto-encoder for anomaly detection of retinal imaging.
%A Luo Y
%A Ma Y
%A Yang Z
%J Phys Eng Sci Med
%V 47
%N 2
%D 2024 Jun 29
%M 38285270
%F 7.099
%R 10.1007/s13246-023-01381-x
%X Identifying unknown types of diseases is a crucial step in preceding retinal imaging classification for the sake of safety, which is known as anomaly detection of retinal imaging. However, the widely-used supervised learning algorithms are not suitable for this problem, since the data of the unknown category is unobtainable. Moreover, for retinal imaging with different types of anomalous regions, using a single-resolution input causes information loss. Therefore, we propose an unsupervised auto-encoder model with multi-resolution inputs and outputs. We provide a theoretical understanding of the effectiveness of reconstruction error and the improvement of self-supervised learning for anomaly detection. Our experiments on two widely-used retinal imaging datasets show that the proposed methods are superior to other methods, and further experiments verify the validity of each part of the proposed method.