Segmented Masks for Best-Performing Models in A U-Net-Based Approach for Histological Tissue Segmentation Using RCAug Data Augmentation

This dataset contains the segmentation masks corresponding to the best-performing model and augmentation configurations reported in the paper “A U-Net-Based Approach for Histological Tissue Segmentation Using RCAug Data Augmentation” (SIBGRAPI 2025, IEEE Xplore).

For each histology dataset, we provide the predicted masks generated by the top-performing U-Net-based models under the augmentation strategies highlighted in the article, together with concise summary metrics. These files are intended to support result inspection, comparison and reuse in further studies.

本数据集包含论文《基于U-Net的RCAug数据增强组织病理学组织分割方法》（发表于2025年SIBGRAPI会议，IEEE Xplore收录）中报告的最优性能模型与增强配置对应的分割掩码。

针对每一组组织病理学数据集，我们提供了本文重点阐述的增强策略下，由最优性能U-Net基模型生成的预测掩码，并附带简洁的汇总统计指标。本数据集文件旨在支持研究人员开展结果检视、对比分析，并为后续研究中的成果复用提供便利。