Transparency guided ensemble convolutional neural networks for stratification of pseudoprogression and true progression of glioblastoma multiform

Abstract

Pseudoprogression (PsP) is an imitation of true tumor progression (TTP) in patients with glioblastoma multiform (GBM). Differentiating them is a challenging and time-consuming task for radiologists. Although deep neural networks can automatically diagnose PsP and TTP, interpretability shortage is always the heel of Achilles. To overcome these shortcomings and win the trust of physician, we propose a transparency guided ensemble convolutional neural network to automatically stratify PsP and TTP on magnetic resonance imaging (MRI). A total of 84 patients with GBM are enrolled in the study. First, three typical convolutional neutral networks (CNNs) -- VGG, ResNet and DenseNet -- are trained to distinguish PsP and TTP on the dataset. Subsequently, we use the class-specific gradient information from convolutional layers to highlight the important regions in MRI. Radiological experts are then recruited to select the most lesion-relevant layer of each CNN. Finally, the selected layers are utilized to guide the construction of multi-scale ensemble CNN. The classified accuracy of the presented network is 90.20%, the promotion of specificity reaches more than 20%. The results demonstrate that network transparency and ensemble can enhance the reliability and accuracy of CNNs. The presented network is promising for the diagnosis of PsP and TTP.