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arXiv · 1907.01253

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

Erdi Çallı·Keelin Murphy·Ecem Sogancioglu·Bram van Ginneken
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Abstract

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.

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Erdi Çallı, Keelin Murphy, Ecem Sogancioglu, Bram van Ginneken. 2019-07-02. FRODO: Free rejection of out-of-distribution samples: application to chest x-ray analysis. https://arxiv.org/abs/1907.01253

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