Enlarged Training Dataset by Pairwise GANs for Molecular-Based Brain Tumor Classification
Artikel i vetenskaplig tidskrift, 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.

multi-modal

deep learning

data augmentation

MRI

glioma

molecular-based brain tumor subtype classification

generative adversarial networks

Författare

Chenjie Ge

Chalmers, Elektroteknik, Signalbehandling och medicinsk teknik, Digitala bildsystem och bildanalys

Irene Yu-Hua Gu

Chalmers, Elektroteknik

Asgeir Store Jakola

Sahlgrenska Academy

Jie Yang

Shanghai Jiao Tong University

IEEE Access

2169-3536 (ISSN)

Vol. 8 1 22560-22570

Ämneskategorier

Annan data- och informationsvetenskap

Elektroteknik och elektronik

Datorseende och robotik (autonoma system)

Medicinsk bildbehandling

Styrkeområden

Hälsa och teknik

DOI

10.1109/ACCESS.2020.2969805

Mer information

Senast uppdaterat

2020-02-13