Exploiting Structural Consistency of Chest Anatomy for Unsupervised
  Anomaly Detection in Radiography Images
Tiange Xiang Yixiao Zhang Yongyi Lu Alan Yuille Chaoyi Zhang Weidong Cai Zongwei Zhou
Radiography imaging protocols focus on particular body regions, therefore
producing images of great similarity and yielding recurrent anatomical
structures across patients. Exploiting this structured information could
potentially ease the detection of anomalies from radiography images. To this
end, we propose a Simple Space-Aware Memory Matrix for In-painting and
Detecting anomalies from radiography images (abbreviated as SimSID). We
formulate anomaly detection as an image reconstruction task, consisting of a
space-aware memory matrix and an in-painting block in the feature space. During
the training, SimSID can taxonomize the ingrained anatomical structures into
recurrent visual patterns, and in the inference, it can identify anomalies
(unseen/modified visual patterns) from the test image. Our SimSID surpasses the
state of the arts in unsupervised anomaly detection by +8.0%, +5.0%, and +9.9%
AUC scores on ZhangLab, COVIDx, and CheXpert benchmark datasets, respectively.
Code: https://github.com/MrGiovanni/SimSID