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

Key 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 ap...

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Bibliographic Details
Published in:International forum of allergy & rhinology 2024-09, Vol.14 (9), p.1513-1516
Main Authors: Hsu, Yen‐Chi, Lin, Kao‐Tsung, Lee, Ming‐Sui, Shen, Li‐Sung, Yeh, Te‐Huei, Lin, Yi‐Tsen
Format: Article
Language:English
Subjects:
chronic rhinosinusitis
convolutional neural network
eosinophils
Eosinophils - pathology
Humans
Image processing
Image Processing, Computer-Assisted - methods
Leukocytes (eosinophilic)
Machine Learning
multiple instance learning
Paranasal Sinuses - diagnostic imaging
Paranasal Sinuses - pathology
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Summary:Key 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.
ISSN:2042-6976
2042-6984
2042-6984
DOI:10.1002/alr.23365