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In this work, we propose a method to reject out-of-distribution samples whichcan be adapted to any network architecture and requires no additional trainingdata. Publicly available chest x-ray data (38,353 images) is used to train astandard ResNet-50 model to detect emphysema. Feature activations ofintermediate layers are used as descriptors defining the training datadistribution. A novel metric, FRODO, is measured by using the Mahalanobisdistance of a new test sample to the training data distribution. The method istested 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 orientationor anatomy. In classifying test samples as in or out-of distribution, ourmethod achieves an AUC score of 0.99.

FRODO: Free rejection of out-of-distribution samples: application to chest x-ray analysis