Abstract
The purpose of this study was to develop a computerized classification method for molecular subtypes in low-grade gliomas (LGGs) with multi-scale 3D-at-tention branch networks analyzing multi-sequence brain MRI images. Our dataset consisted of brain T1-weighted and T2-weighted MRI im-ages for 217 patients (58 Astrocytoma IDH-mutant, 49 Astrocytoma IDH-wildtype, and 110 Oligodendroglioma). The proposed method was constructed from a feature extractor, an attention branch, and a perception branch. In the feature extractor, the feature maps were extracted from brain T1-weighted and T2-weighted MRI images, respectively. The attention branch focused on a tumor region and generated the attention maps normalized to 0.0 - 1.0. The feature maps were then multiplied by the attention maps to weight features on LGG in the feature maps. The molecular subtype in LGG was evaluated in the perception branch. The classification accuracy for the proposed method was 63.6%, showing an improvement when compared with the conven-tional method using only single sequence (T2-weighted) MRI images (59.9%).
Published Version
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