  Unsupervised anomaly segmentation approaches to pathology segmentation train
a model on images of healthy subjects, that they define as the 'normal' data
distribution. At inference, they aim to segment any pathologies in new images
as 'anomalies', as they exhibit patterns that deviate from those in 'normal'
training data. Prevailing methods follow the 'corrupt-and-reconstruct'
paradigm. They intentionally corrupt an input image, reconstruct it to follow
the learned 'normal' distribution, and subsequently segment anomalies based on
reconstruction error. Corrupting an input image, however, inevitably leads to
suboptimal reconstruction even of normal regions, causing false positives. To
alleviate this, we propose a novel iterative spatial mask-refining strategy
IterMask2. We iteratively mask areas of the image, reconstruct them, and update
the mask based on reconstruction error. This iterative process progressively
adds information about areas that are confidently normal as per the model. The
increasing content guides reconstruction of nearby masked areas, improving
reconstruction of normal tissue under these areas, reducing false positives. We
also use high-frequency image content as an auxiliary input to provide
additional structural information for masked areas. This further improves
reconstruction error of normal in comparison to anomalous areas, facilitating
segmentation of the latter. We conduct experiments on several brain lesion
datasets and demonstrate effectiveness of our method. Code is available at:
https://github.com/ZiyunLiang/IterMask2
