Shaoguo Cui
ABSTRACT
Noninvasive prediction of glioma CDKN2A/B gene homozygous deletion can assist doctors in preoperative diagnosis of glioma patients, which is crucial for guiding prognosis and the best therapeutic regime. At present, the task of glioma gene classification has the problem of too many parameters and high computational complexity. It can not capture the spatial information of different scale channels, and the introduced attention can only capture local information. Aiming at this problem, a ConvNeXt glioma gene classification method integrating pyramid channel attention is proposed. Firstly, so as to reduce network parameters, group convolution is used to extract information of different scale channel. Secondly, in order to learn important features, attention mechanism is introduced. Finally, the obtained weights are weighted to each channel to recalibrate the feature map and stablish a long-term dependence. Multiparametric brain magnetic resonance imaging (MRI) data and corresponding genomic information of 127 subjects (68 positives and 57 negatives for CDKN2A/B homozygous deletion) were obtained from The Cancer Imaging Archive (TCIA) and The Cancer Genome Atlas (TCGA) respectively. The experimental results certify that proposed method achieves 83.34%, 81.12% and 84.62% on ACC, AUC and F1, respectively. Compared with the baseline model ConvNeXt, the classification performance is improved, which proves the effectiveness and efficiency of the proposed model in the CDKN2A/B gene classification task of glioma.
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