Enlarged Training Dataset by Pairwise GANs for Molecular-Based Brain Tumor Classification
Journal article, 2020

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.

molecular-based brain tumor subtype classification



deep learning

generative adversarial networks


data augmentation


Chenjie Ge

Chalmers, Electrical Engineering, Signalbehandling och medicinsk teknik, Imaging and Image Analysis

Irene Yu-Hua Gu

Chalmers, Electrical Engineering

Asgeir Store Jakola

University of Gothenburg

Jie Yang

Shanghai Jiao Tong University

IEEE Access

2169-3536 (ISSN)

Vol. 8 1 22560-22570

Subject Categories

Other Computer and Information Science

Electrical Engineering, Electronic Engineering, Information Engineering

Computer Vision and Robotics (Autonomous Systems)

Medical Image Processing

Areas of Advance

Health Engineering



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