  Many types of anomaly detection methods have been proposed recently, and
applied to a wide variety of fields including medical screening and production
quality checking. Some methods have utilized images, and, in some cases, a part
of the anomaly images is known beforehand. However, this kind of information is
dismissed by previous methods, because the methods can only utilize a normal
pattern. Moreover, the previous methods suffer a decrease in accuracy due to
negative effects from surrounding noises. In this study, we propose a
spatially-weighted anomaly detection method (SPADE) that utilizes all of the
known patterns and lessens the vulnerability to ambient noises by applying
Grad-CAM, which is the visualization method of a CNN. We evaluated our method
quantitatively using two datasets, the MNIST dataset with noise and a dataset
based on a brief screening test for dementia.
