Radiomics and state-of-art convolutional neural networks (CNNs) have demonstrated their usefulness for predicting genotype in gliomas from brain MRI. However, these techniques rely on accurate tumor segmentation and do not facilitate insights into the critical discrimative features. To mitigate this, we employ a novel technique called CNNs with discriminative localization (DL-CNN) on a clinical T2 weighted MRI dataset of IDH1 mutant and wild-type tumor patients, which is not only free of tumor segmentation with high classification accuracy of 86.7% but also demonstrates that the tumoral area is discriminative in mutants while in IDH1 wildtype the peri-tumoral edema is also involved.
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