Multiple instance learning for eosinophil quantification of sinonasal histopathology images: A hierarchical determination on whole slide images

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...

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Bibliographic Details
Published inInternational forum of allergy & rhinology
Main Authors Hsu, Yen-Chi, Lin, Kao-Tsung, Lee, Ming-Sui, Shen, Li-Sung, Yeh, Te-Huei, Lin, Yi-Tsen
Format Journal Article
LanguageEnglish
Published United States 01.09.2024
Subjects
chronic rhinosinusitis
eosinophils
convolutional neural network
multiple instance learning
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Summary: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.
ISSN:2042-6984
DOI:10.1002/alr.23365