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.

翻译：胸部X光图像通常通过降低分辨率和位深来压缩至更小尺寸，这一过程可能改变细微的诊断特征。放射科医师常使用窗宽窗位操作来增强图像对比度，但此类操作对胸部X光分类性能的影响尚不明确。本研究发现，窗宽窗位操作可提升胸部X光分类性能，并提出一种可学习最优窗宽窗位的模型WindowNet。我们首先探究位深对分类性能的影响，发现高位深（12位）能带来更优效果。随后评估不同窗宽窗位设置，表明使用特定窗口进行训练通常可改善病理学分类性能。最后，我们提出并评估了可学习最优窗宽窗位的WindowNet模型，证明其相较未使用窗宽窗位的基线模型，分类性能获得显著提升。