  In this work, we propose a method to reject out-of-distribution samples which
can be adapted to any network architecture and requires no additional training
data. Publicly available chest x-ray data (38,353 images) is used to train a
standard ResNet-50 model to detect emphysema. Feature activations of
intermediate layers are used as descriptors defining the training data
distribution. A novel metric, FRODO, is measured by using the Mahalanobis
distance of a new test sample to the training data distribution. The method is
tested using a held-out test dataset of 21,176 chest x-rays (in-distribution)
and a set of 14,821 out-of-distribution x-ray images of incorrect orientation
or anatomy. In classifying test samples as in or out-of distribution, our
method achieves an AUC score of 0.99.

Machine Learning;Image and Video Processing;Machine Learning