Higher Chest X-ray Resolution Improves Classification Performance

Alessandro Wollek, Sardi Hyska, Bastian Sabel, Michael Ingrisch, Tobias Lasser
6/9/2023
cs.CV

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Abstract

Deep learning models for image classification are often trained at a resolution of 224 x 224 pixels for historical and efficiency reasons. However, chest X-rays are acquired at a much higher resolution to display subtle pathologies. This study investigates the effect of training resolution on chest X-ray classification performance, using the chest X-ray 14 dataset. The results show that training with a higher image resolution, specifically 1024 x 1024 pixels, results in the best overall classification performance with a mean AUC of 84.2 % compared to 82.7 % when trained with 256 x 256 pixel images. Additionally, comparison of bounding boxes and GradCAM saliency maps suggest that low resolutions, such as 256 x 256 pixels, are insufficient for identifying small pathologies and force the model to use spurious discriminating features. Our code is publicly available at https://gitlab.lrz.de/IP/cxr-resolution

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Cite this paper

@article{wollek2023higher,
  title  = {Higher Chest X-ray Resolution Improves Classification Performance},
  author = {Alessandro Wollek and Sardi Hyska and Bastian Sabel and Michael Ingrisch and Tobias Lasser},
  year   = {2023},
  eprint = {2306.06051},
  archivePrefix = {arXiv},
  url    = {http://arxiv.org/abs/2306.06051v2}
}

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