The primary objective of this research was to enhance the quality of semantic segmentation in cytology images by incorporating super-resolution (SR) architectures. An additional contribution was the development of a novel dataset aimed at improving imaging quality in the presence of inaccurate focus. Our experimental results demonstrate that the integration of SR techniques into the segmentation pipeline can lead to a significant improvement of up to 25% in the mean average precision (mAP) segmentation metric. These findings suggest that leveraging SR architectures holds great promise for advancing the state of the art in cytology image analysis.

翻译：本研究的主要目标是通过集成超分辨率（SR）架构来提升细胞学图像语义分割的质量。另一项贡献是开发了一个新的数据集，旨在改善焦点不准确情况下的成像质量。实验结果表明，将SR技术整合到分割流程中，可使分割指标平均精度（mAP）显著提升高达25%。这些发现表明，利用SR架构在推动细胞学图像分析领域前沿发展方面具有巨大潜力。