Downsampling images and labels, often necessitated by limited resources or to
expedite network training, leads to the loss of small objects and thin
boundaries. This undermines the segmentation network's capacity to interpret
images accurately and predict detailed labels, resulting in diminished
performance compared to processing at original resolutions. This situation
exemplifies the trade-off between efficiency and accuracy, with higher
downsampling factors further impairing segmentation outcomes. Preserving
information during downsampling is especially critical for medical image
segmentation tasks. To tackle this challenge, we introduce a novel method named
Edge-preserving Probabilistic Downsampling (EPD). It utilizes class uncertainty
within a local window to produce soft labels, with the window size dictating
the downsampling factor. This enables a network to produce quality predictions
at low resolutions. Beyond preserving edge details more effectively than
conventional nearest-neighbor downsampling, employing a similar algorithm for
images, it surpasses bilinear interpolation in image downsampling, enhancing
overall performance. Our method significantly improved Intersection over Union
(IoU) to 2.85%, 8.65%, and 11.89% when downsampling data to 1/2, 1/4, and 1/8,
respectively, compared to conventional interpolation methods.

通过引入一种名为 Edge-preserving Probabilistic Downsampling (EPD) 的新方法，该方法利用局部窗口中的类别不确定性产生软标签，以低分辨率实现网络生成高质量预测，从而更有效地保留边缘细节，并在图像下采样中优于常规的最近邻插值方法和双线性插值方法，从而提高整体性能。相较于常规插值方法，我们的方法在将数据下采样至 1/2、1/4 和 1/8 时，Intersection over Union (IoU) 显著提高了 2.85%、8.65% 和 11.89%。