  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.
