MMMNA-Net for Overall Survival Time Prediction of Brain Tumor Patients

TL;DR

This paper introduces MMMNA-Net, a novel deep learning model that improves brain tumor survival time prediction from multimodal MRI by effectively fusing multi-scale features, achieving significant accuracy gains and robustness to missing data.

Contribution

The paper presents a new multi-scale nonlocal feature fusion module for multimodal MRI analysis, enhancing survival prediction accuracy and handling missing modalities.

Findings

8.76% accuracy improvement over state-of-the-art

Effective handling of missing modalities

Robust multi-scale feature fusion

Abstract

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.