Multiple Instance Learning Using Reduced Deep Learning Model For Glioma Subtype Classification

Glioma is a malignant brain tumor that occurs in the central nervous system and is classified into high-grade glioblastoma multiforme (GBM) and low-grade glioma (LGG). GBM is a fatal malignant tumor with an extremely poor prognosis. In recent years, numerous studies have focused on the automatic classification of gliomas using histopathological images for diagnostic support. Among these, methods using multiple instance learning (MIL) have become mainstream. Many previous studies use pre-trained deep learning models as feature extractors for MIL. MIL processes multiple patch images at once, making low-cost end-to-end learning difficult. Therefore, we propose a MIL model using a reduced feature extractor. We use MobileNetV4 as the feature extractor and reduce its intermediate blocks to achieve a lightweight model. In this study, we incorporated the reduced feature extractor into five MIL models and compared the classification performance and computational cost. Experimental results using histopathological images of diffuse glioma patients from the Cancer Genome Atlas (TCGA) showed that the reduced models demonstrated classification performance equal to or better than the original models. Additionally, the reduced models have significantly lower computational costs and can easily achieve end-to-end learning. Therefore, our method is suggested to be useful for low-cost, end-to-end MIL classification using histopathological images.