Yen‐Chi HsuKao‐Tsung LinMING-SUI LEELi‐Sung ShenYI-TSEN LINTE-HUEI YEH2024-06-272024-06-272024-05-20https://scholars.lib.ntu.edu.tw/handle/123456789/719496Key points: 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.[SDGs]SDG3[SDGs]SDG17journal article10.1002/alr.23365