Deep semi-supervised learning for brain tumor classification
Journal article, 2020

Background: This paper addresses issues of brain tumor, glioma, classification from four modalities of Magnetic Resonance Image (MRI) scans (i.e., T1 weighted MRI, T1 weighted MRI with contrast-enhanced, T2 weighted MRI and FLAIR). Currently, many available glioma datasets often contain some unlabeled brain scans, and many datasets are moderate in size.
Methods: We propose to exploit deep semi-supervised learning to make full use of the unlabeled data. Deep CNN features were incorporated into a new graph-based semi-supervised learning framework for learning the labels of the unlabeled data, where a new 3D-2D consistent constraint is added to make consistent classifications for the 2D slices from the same 3D brain scan. A deep-learning classifier is then trained to classify different glioma types using both labeled and unlabeled data with estimated labels. To alleviate the overfitting caused by moderate-size datasets, synthetic MRIs generated by Generative Adversarial Networks (GANs) are added in the training of CNNs.
Results: The proposed scheme has been tested on two glioma datasets, TCGA dataset for IDH-mutation prediction (molecular-based glioma subtype classification) and MICCAI dataset for glioma grading. Our results have shown good performance (with test accuracies 86.53% on TCGA dataset and 90.70% on MICCAI dataset). Conclusions: The proposed scheme is effective for glioma IDH-mutation prediction and glioma grading, and its performance is comparable to the state-of-the-art.

Molecular-based brain tumor classification

Deep learning




Semi-supervised learning


Chenjie Ge

Chalmers, Electrical Engineering, Signal Processing and Biomedical Engineering

Irene Yu-Hua Gu

Chalmers, Electrical Engineering

Asgeir Store Jakola

Sahlgrenska University Hospital

Jie Yang

Shanghai Jiao Tong University

BMC Medical Imaging

14712342 (eISSN)

Vol. 20 1 87

Subject Categories

Other Medical Engineering

Radiology, Nuclear Medicine and Medical Imaging

Computer Vision and Robotics (Autonomous Systems)

Areas of Advance

Health Engineering

Life Science Engineering (2010-2018)





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9/1/2020 1