  Chest X-ray (CXR) images are commonly compressed to a lower resolution and
bit depth to reduce their size, potentially altering subtle diagnostic
features.
  Radiologists use windowing operations to enhance image contrast, but the
impact of such operations on CXR classification performance is unclear.
  In this study, we show that windowing can improve CXR classification
performance, and propose WindowNet, a model that learns optimal window
settings.
  We first investigate the impact of bit-depth on classification performance
and find that a higher bit-depth (12-bit) leads to improved performance.
  We then evaluate different windowing settings and show that training with a
distinct window generally improves pathology-wise classification performance.
  Finally, we propose and evaluate WindowNet, a model that learns optimal
window settings, and show that it significantly improves performance compared
to the baseline model without windowing.
