Deep semi-supervised learning for brain tumor classification
Artikel i vetenskaplig tidskrift, 2020
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
Chalmers, Elektroteknik, Signalbehandling och medicinsk teknik
Irene Yu-Hua Gu
Asgeir Store Jakola
Shanghai Jiao Tong University
BMC Medical Imaging
1471-2342 (ISSN)Vol. 20 1 87
Radiologi och bildbehandling
Datorseende och robotik (autonoma system)
Hälsa och teknik
Livsvetenskaper och teknik (2010-2018)