Automated tumor detection in Digital Breast Tomosynthesis (DBT) is a
difficult task due to natural tumor rarity, breast tissue variability, and high
resolution. Given the scarcity of abnormal images and the abundance of normal
images for this problem, an anomaly detection/localization approach could be
well-suited. However, most anomaly localization research in machine learning
focuses on non-medical datasets, and we find that these methods fall short when
adapted to medical imaging datasets. The problem is alleviated when we solve
the task from the image completion perspective, in which the presence of
anomalies can be indicated by a discrepancy between the original appearance and
its auto-completion conditioned on the surroundings. However, there are often
many valid normal completions given the same surroundings, especially in the
DBT dataset, making this evaluation criterion less precise. To address such an
issue, we consider pluralistic image completion by exploring the distribution
of possible completions instead of generating fixed predictions. This is
achieved through our novel application of spatial dropout on the completion
network during inference time only, which requires no additional training cost
and is effective at generating diverse completions. We further propose minimum
completion distance (MCD), a new metric for detecting anomalies, thanks to
these stochastic completions. We provide theoretical as well as empirical
support for the superiority over existing methods of using the proposed method
for anomaly localization. On the DBT dataset, our model outperforms other
state-of-the-art methods by at least 10\% AUROC for pixel-level detection.

本文提出了一种通过图像完成的角度解决 Digital Breast Tomosynthesis（DBT）自动肿瘤检测问题的方法，该方法通过探索可能完成的分布来解决固定预测的问题，并提出 “最小完成距离（MCD）” 来检测异常，相对于现有方法在像素级检测的 AUROC 上表现优异。