Current dataset mask processing operations relies on interpolation algorithms that do not produce extra pixels, such as nearest neighbor (NN) interpolation, as opposed to algorithms that do produce extra pixels, like bicubic (BIC) or bilinear (BIL) interpolation. In our previous study, the author proposed an alternative approach to NN-based mask processing and evaluated its effects on deep learning training outcomes. In this study, the author evaluated the effects of both BIC-based image and mask processing and BIC-and-NN-based image and mask processing versus NN-based image and mask processing. The evaluation revealed that the BIC-BIC model/network was an 8.9578 % (with image size 256 x 256) and a 1.0496 % (with image size 384 x 384) increase of the NN-NN network compared to the NN-BIC network which was an 8.3127 % (with image size 256 x 256) and a 0.2887 % (with image size 384 x 384) increase of the NN-NN network.

翻译：当前数据集掩膜处理操作依赖的插值算法（如最近邻插值）不会产生额外像素，这与双三次或双线性插值等会产生额外像素的算法形成对比。在先前研究中，作者提出了一种基于最近邻插值的掩膜处理替代方案，并评估了其对深度学习训练效果的影响。本研究评估了基于双三次插值的图像与掩膜联合处理、以及双三次插值与最近邻插值混合处理相比最近邻插值处理的效果差异。评估结果显示：采用双三次-双三次插值方案的模型/网络相较最近邻-最近邻网络，在图像尺寸256×256时性能提升8.9578%，在384×384时提升1.0496%；而最近邻-双三次混合方案相比最近邻-最近邻网络，在256×256时提升8.3127%，在384×384时提升0.2887%。