MMMNA-Net for Overall Survival Time Prediction of Brain Tumor Patients
This work addresses a critical need in medical imaging for more accurate glioma prognosis, though it is incremental as it builds on existing deep learning methods by enhancing feature fusion.
The paper tackles the problem of predicting overall survival time for brain tumor patients using multimodal MRI scans by proposing a novel method with improved nonlocal features fusion across different scales, achieving an 8.76% relative improvement in accuracy over the state-of-the-art method (0.6989 vs. 0.6426).
Overall survival (OS) time is one of the most important evaluation indices for gliomas situations. Multimodal Magnetic Resonance Imaging (MRI) scans play an important role in the study of glioma prognosis OS time. Several deep learning-based methods are proposed for the OS time prediction on multi-modal MRI problems. However, these methods usually fuse multi-modal information at the beginning or at the end of the deep learning networks and lack the fusion of features from different scales. In addition, the fusion at the end of networks always adapts global with global (eg. fully connected after concatenation of global average pooling output) or local with local (eg. bilinear pooling), which loses the information of local with global. In this paper, we propose a novel method for multi-modal OS time prediction of brain tumor patients, which contains an improved nonlocal features fusion module introduced on different scales. Our method obtains a relative 8.76% improvement over the current state-of-art method (0.6989 vs. 0.6426 on accuracy). Extensive testing demonstrates that our method could adapt to situations with missing modalities. The code is available at https://github.com/TangWen920812/mmmna-net.