تقرير
FRODO: Free rejection of out-of-distribution samples: application to chest x-ray analysis
| العنوان: | FRODO: Free rejection of out-of-distribution samples: application to chest x-ray analysis |
|---|---|
| المؤلفون: | Çallı, Erdi, Murphy, Keelin, Sogancioglu, Ecem, van Ginneken, Bram |
| سنة النشر: | 2019 |
| المجموعة: | Computer Science Statistics |
| مصطلحات موضوعية: | Computer Science - Machine Learning, Electrical Engineering and Systems Science - Image and Video Processing, Statistics - Machine Learning |
| الوصف: | 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. Comment: MIDL 2019 [arXiv:1907.08612] |
| نوع الوثيقة: | Working Paper |
| URL الوصول: | http://arxiv.org/abs/1907.01253 |
| رقم الأكسشن: | edsarx.1907.01253 |
| قاعدة البيانات: | arXiv |
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