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

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