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

Grading

Glioma

MRI

Semi-supervised learning

Author

Chenjie Ge

Chalmers, Electrical Engineering, Signal Processing and Biomedical Engineering, Imaging and Image Analysis

Irene Yu-Hua Gu

Chalmers, Electrical Engineering

Asgeir Store Jakola

Sahlgrenska University Hospital

Jie Yang

Shanghai Jiao Tong University

BMC Medical Imaging

1471-2342 (ISSN)

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)

DOI

10.1186/s12880-020-00485-0

PubMed

32727476

More information

Latest update

9/1/2020 1