基于模糊排序的晚期融合技术用于细胞学图像分割
Fuzzy Rank-based Late Fusion Technique for Cytology image Segmentation
AI总结：
本文提出一种基于模糊排序的晚期融合技术，整合UNet、SegNet和PSPNet模型，在宫颈和乳腺细胞学图像分割上分别达到84.27%和83.79%的MeanIoU，优于传统融合规则。
摘要：
细胞学图像分割因其复杂的细胞结构和多个重叠区域而颇具挑战性。另一方面，对于监督机器学习技术，我们需要大量标注数据，这代价高昂。近年来，晚期融合技术在图像分类领域展现出一些有前景的性能。在本文中，我们探索了一种基于模糊的晚期融合技术用于细胞学图像分割。该融合规则整合了三种传统语义分割模型：UNet、SegNet和PSPNet。该技术应用于两个细胞学图像数据集，即宫颈细胞学（HErlev）和乳腺细胞学（JUCYT-v1）图像数据集。在提出的晚期融合技术之后，我们在HErlev数据集和JUCYT-v1数据集上分别取得了最大MeanIoU分数84.27%和83.79%，这优于传统的融合规则，如平均概率、几何平均、Borda计数等。所提出模型的代码可在GitHub上获取。
英文摘要：
Cytology image segmentation is quite challenging due to its complex cellular structure and multiple overlapping regions. On the other hand, for supervised machine learning techniques, we need a large amount of annotated data, which is costly. In recent years, late fusion techniques have given some promising performances in the field of image classification. In this paper, we have explored a fuzzy-based late fusion techniques for cytology image segmentation. This fusion rule integrates three traditional semantic segmentation models UNet, SegNet, and PSPNet. The technique is applied on two cytology image datasets, i.e., cervical cytology(HErlev) and breast cytology(JUCYT-v1) image datasets. We have achieved maximum MeanIoU score 84.27% and 83.79% on the HErlev dataset and JUCYT-v1 dataset after the proposed late fusion technique, respectively which are better than that of the traditional fusion rules such as average probability, geometric mean, Borda Count, etc. The codes of the proposed model are available on GitHub.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
