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Enlarged Training Dataset by Pairwise GANs for Molecular-Based Brain Tumor Classification

Journal article
Authors Chenjie Ge
Irene Yu-Hua Gu
Asgeir Store Jakola
Jie Yang
Published in IEEE Access
Volume 8
Pages 22560 - 22570
Publication year 2020
Published at Institute of Neuroscience and Physiology, Department of Clinical Neuroscience
Pages 22560 - 22570
Language en
Links https://ieeexplore.ieee.org/documen...
Subject categories Medical Image Processing, Signal Processing, Cancer and Oncology, Diagnostic radiology, Neurosurgery, Neurology

Abstract

This paper addresses issues of brain tumor subtype classification using Magnetic Resonance Images (MRIs) from different scanner modalities like T1 weighted, T1 weighted with contrast-enhanced, T2 weighted and FLAIR images. Currently most available glioma datasets are relatively moderate in size, and often accompanied with incomplete MRIs in different modalities. To tackle the commonly encountered problems of insufficiently large brain tumor datasets and incomplete modality of image for deep learning, we propose to add augmented brain MR images to enlarge the training dataset by employing a pairwise Generative Adversarial Network (GAN) model. The pairwise GAN is able to generate synthetic MRIs across different modalities. To achieve the patient-level diagnostic result, we propose a post-processing strategy to combine the slice-level glioma subtype classification results by majority voting. A two-stage course-to-fine training strategy is proposed to learn the glioma feature using GAN-augmented MRIs followed by real MRIs. To evaluate the effectiveness of the proposed scheme, experiments have been conducted on a brain tumor dataset for classifying glioma molecular subtypes: isocitrate dehydrogenase 1 (IDH1) mutation and IDH1 wild-type. Our results on the dataset have shown good performance (with test accuracy 88.82%). Comparisons with several state-of-the-art methods are also included.

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