Abstract：
CONCLUSIONS: We proposed a hierarchical framework including an unsupervised candidate image selection and a weakly supervised patch image detection based on multiple instance learning (MIL) to effectively estimate eosinophil quantities in tissue samples from whole slide images. MIL is an innovative approach that can help deal with the variability in cell distribution detection and enable automated eosinophil quantification from sinonasal histopathological images with a high degree of accuracy. The study lays the foundation for further research and development in the field of automated histopathological image analysis, and validation on more extensive and diverse datasets will contribute to real-world application.
摘要：
结论：我们提出了一个分层框架，包括无监督的候选图像选择和基于多实例学习（MIL）的弱监督块图像检测，以有效地估计整个幻灯片图像的组织样本中的嗜酸性粒细胞数量。MIL是一种创新的方法，可以帮助处理细胞分布检测中的变异性，并能够以高精度从鼻窦组织病理学图像中自动定量嗜酸性粒细胞。该研究为自动化组织病理学图像分析领域的进一步研究和发展奠定了基础,和验证更广泛和多样化的数据集将有助于现实世界的应用。
