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| Main Authors: | , , , , |
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| Format: | Preprint |
| Published: |
2022
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2206.06267 |
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| _version_ | 1866911844238622720 |
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| author | Tang, Wen Zhang, Haoyue Yu, Pengxin Kang, Han Zhang, Rongguo |
| author_facet | Tang, Wen Zhang, Haoyue Yu, Pengxin Kang, Han Zhang, Rongguo |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2206_06267 |
| institution | arXiv |
| publishDate | 2022 |
| record_format | arxiv |
| spellingShingle | MMMNA-Net for Overall Survival Time Prediction of Brain Tumor Patients Tang, Wen Zhang, Haoyue Yu, Pengxin Kang, Han Zhang, Rongguo Image and Video Processing Artificial Intelligence Computer Vision and Pattern Recognition 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. |
| title | MMMNA-Net for Overall Survival Time Prediction of Brain Tumor Patients |
| topic | Image and Video Processing Artificial Intelligence Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2206.06267 |