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

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Ge, Chenjie,1991Chalmers tekniska högskola,Chalmers University of Technology (author)

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

Article/chapterEnglish2020

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2020
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LIBRIS-ID:oai:research.chalmers.se:cbd53062-3958-4f65-9ea8-a5ac8fd09248
https://research.chalmers.se/publication/516637URI
https://research.chalmers.se/publication/515421URI
https://doi.org/10.1109/ACCESS.2020.2969805DOI
https://gup.ub.gu.se/publication/289840URI

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Language:English
Summary in:English

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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.

Subject headings and genre

NATURVETENSKAP Data- och informationsvetenskap Annan data- och informationsvetenskap hsv//swe
NATURAL SCIENCES Computer and Information Sciences Other Computer and Information Science hsv//eng
TEKNIK OCH TEKNOLOGIER Elektroteknik och elektronik hsv//swe
ENGINEERING AND TECHNOLOGY Electrical Engineering, Electronic Engineering, Information Engineering hsv//eng
NATURVETENSKAP Data- och informationsvetenskap Datorseende och robotik hsv//swe
NATURAL SCIENCES Computer and Information Sciences Computer Vision and Robotics hsv//eng
TEKNIK OCH TEKNOLOGIER Medicinteknik Medicinsk bildbehandling hsv//swe
ENGINEERING AND TECHNOLOGY Medical Engineering Medical Image Processing hsv//eng
MEDICIN OCH HÄLSOVETENSKAP Klinisk medicin Kirurgi hsv//swe
MEDICAL AND HEALTH SCIENCES Clinical Medicine Surgery hsv//eng
MEDICIN OCH HÄLSOVETENSKAP Klinisk medicin Radiologi och bildbehandling hsv//swe
MEDICAL AND HEALTH SCIENCES Clinical Medicine Radiology, Nuclear Medicine and Medical Imaging hsv//eng
MEDICIN OCH HÄLSOVETENSKAP Klinisk medicin Neurologi hsv//swe
MEDICAL AND HEALTH SCIENCES Clinical Medicine Neurology hsv//eng
MEDICIN OCH HÄLSOVETENSKAP Klinisk medicin Cancer och onkologi hsv//swe
MEDICAL AND HEALTH SCIENCES Clinical Medicine Cancer and Oncology hsv//eng
TEKNIK OCH TEKNOLOGIER Elektroteknik och elektronik Signalbehandling hsv//swe
ENGINEERING AND TECHNOLOGY Electrical Engineering, Electronic Engineering, Information Engineering Signal Processing hsv//eng
molecular-based brain tumor subtype classification
glioma
MRI
deep learning
generative adversarial networks
multi-modal
data augmentation

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Gu, Irene Yu-Hua,1953Chalmers tekniska högskola,Chalmers University of Technology(Swepub:cth)irenegu (author)
Jakola, Asgeir StoreGothenburg University,Göteborgs universitet,Institutionen för neurovetenskap och fysiologi, sektionen för klinisk neurovetenskap,Institute of Neuroscience and Physiology, Department of Clinical Neuroscience(Swepub:gu)xjakas (author)
Yang, JieShanghai Jiao Tong University (author)
Chalmers tekniska högskolaInstitutionen för neurovetenskap och fysiologi, sektionen för klinisk neurovetenskap (creator_code:org_t)

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In:IEEE Access8:1, s. 22560-225702169-35362169-3536

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