  Current unsupervised anomaly localization approaches rely on generative
models to learn the distribution of normal images, which is later used to
identify potential anomalous regions derived from errors on the reconstructed
images. However, a main limitation of nearly all prior literature is the need
of employing anomalous images to set a class-specific threshold to locate the
anomalies. This limits their usability in realistic scenarios, where only
normal data is typically accessible. Despite this major drawback, only a
handful of works have addressed this limitation, by integrating supervision on
attention maps during training. In this work, we propose a novel formulation
that does not require accessing images with abnormalities to define the
threshold. Furthermore, and in contrast to very recent work, the proposed
constraint is formulated in a more principled manner, leveraging well-known
knowledge in constrained optimization. In particular, the equality constraint
on the attention maps in prior work is replaced by an inequality constraint,
which allows more flexibility. In addition, to address the limitations of
penalty-based functions we employ an extension of the popular log-barrier
methods to handle the constraint. Last, we propose an alternative
regularization term that maximizes the Shannon entropy of the attention maps,
reducing the amount of hyperparameters of the proposed model. Comprehensive
experiments on two publicly available datasets on brain lesion segmentation
demonstrate that the proposed approach substantially outperforms relevant
literature, establishing new state-of-the-art results for unsupervised lesion
segmentation, and without the need to access anomalous images.
