CheXphotogenic: Generalization of Deep Learning Models for Chest X-ray Interpretation to Photos of Chest X-rays

• URL: http://arxiv.org/abs/2011.06129v1
• Date: Thu, 12 Nov 2020 00:16:51 GMT
• Title: CheXphotogenic: Generalization of Deep Learning Models for Chest X-ray Interpretation to Photos of Chest X-rays
• Authors: Pranav Rajpurkar, Anirudh Joshi, Anuj Pareek, Jeremy Irvin, Andrew Y. Ng, Matthew Lungren
• Abstract summary: We measured the diagnostic performance for 8 different chest x-ray models when applied to photos of chest x-rays. Several models had a drop in performance when applied to photos of chest x-rays, but even with this drop, some models still performed comparably to radiologists.
• Score: 4.396061096553544
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: The use of smartphones to take photographs of chest x-rays represents an appealing solution for scaled deployment of deep learning models for chest x-ray interpretation. However, the performance of chest x-ray algorithms on photos of chest x-rays has not been thoroughly investigated. In this study, we measured the diagnostic performance for 8 different chest x-ray models when applied to photos of chest x-rays. All models were developed by different groups and submitted to the CheXpert challenge, and re-applied to smartphone photos of x-rays in the CheXphoto dataset without further tuning. We found that several models had a drop in performance when applied to photos of chest x-rays, but even with this drop, some models still performed comparably to radiologists. Further investigation could be directed towards understanding how different model training procedures may affect model generalization to photos of chest x-rays.

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